def visit_sum(self, op: d5.ops.Sum, network: ReferenceNetwork):
inputs = []
for i in range(0, len(op.input)):
inputs.append(network.variables[op.input[i]])
network.gradients[op.input[i]] = np.zeros_like(
network.variables[op.input[i]])
Y_shape = np.copy(inputs[0])
for i in range(1, len(inputs)):
Y_shape = Y_shape + inputs[i]
network.variables[op.o_sum] = Y_shape
input_desc = [desc_from_tensor(i) for i in inputs]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes[
'DTYPE'] = 'float' if inputs[0].dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('Sum' + str(self.sum_counter),
SumCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
sumlayer = d5ref.custom_op(opdesc)
self.sum_counter += 1
self.add_node(op, network, sumlayer, self.sum_counter)
self.add_edges(op, network, self.sum_counter)
def visit_globalaveragepool(self, op: d5.ops.GlobalAveragePool,
network: ReferenceNetwork):
X = network.variables[op.i_X]
out_shape = [X.shape[0], X.shape[1]]
for i in range(len(X.shape) - 2):
out_shape = out_shape + [1]
Y_shape = np.zeros(out_shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if X.dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('GlobalAveragePool' +
str(self.globalaveragepool_counter),
GlobalAveragePoolCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
globalaveragepoollayer = d5ref.custom_op(opdesc)
self.globalaveragepool_counter += 1
self.add_node(op, network, globalaveragepoollayer,
self.globalaveragepool_counter)
self.add_edges(op, network, self.globalaveragepool_counter)
def visit_gemm(self, op: d5.ops.Gemm, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
C = network.variables[op.i_C]
transposeA = 0 if op.transA is None else op.transA.get_value()
transposeB = 0 if op.transB is None else op.transB.get_value()
alpha = 1.0 if op.alpha is None else op.alpha.get_value()
beta = 1.0 if op.beta is None else op.beta.get_value()
A_transposed = A.T if transposeA != 0 else A
B_transposed = B.T if transposeB != 0 else B
Y_shape = np.zeros((np.dot(A_transposed, B_transposed)).shape,
dtype=A.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
network.gradients[op.i_C] = np.zeros_like(C)
input_desc = [desc_from_tensor(i) for i in [A, B, C]]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if A.dtype == np.float32 else 'double'
attributes['ALPHA'] = alpha
attributes['BETA'] = beta
attributes['TRANSA'] = transposeA
attributes['TRANSB'] = transposeB
opdesc = d5.compile_custom_cppop('Gemm' + str(self.gemm_counter),
GemmCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
gemmlayer = d5ref.custom_op(opdesc)
self.gemm_counter += 1
self.add_node(op, network, gemmlayer, self.gemm_counter)
self.add_edges(op, network, self.gemm_counter)
def visit_gemm(self, op: d5.ops.Gemm, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
C = network.variables[op.i_C]
transposeA = 0 if op.transA is None else op.transA.get_value()
transposeB = 0 if op.transB is None else op.transB.get_value()
alpha = 1.0 if op.alpha is None else op.alpha.get_value()
beta = 1.0 if op.beta is None else op.beta.get_value()
A_transposed = A.T if transposeA != 0 else A
B_transposed = B.T if transposeB != 0 else B
Y_shape = np.zeros((np.dot(A_transposed, B_transposed)).shape, dtype=A.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
network.gradients[op.i_C] = np.zeros_like(C)
input_desc = [desc_from_tensor(i) for i in [A, B, C]]
output_desc = [desc_from_tensor(Y_shape)]
gemmlayer = GemmOp(
input_desc,
output_desc,
alpha,
beta,
transposeA,
transposeB
)
self.gemm_counter += 1
self.add_node(op, network, gemmlayer, self.gemm_counter)
self.add_edges(op, network, self.gemm_counter)
def visit_maxpool(self, op: d5.ops.MaxPool, network: ReferenceNetwork):
X = network.variables[op.i_X]
auto_pad = 'NOTSET' if op.auto_pad is None else op.auto_pad.get_value()
kernel_shape = op.kernel_shape.get_value()
temp_pads = []
temp_strides = []
for i in range(0, len(kernel_shape)):
temp_pads.append(0)
temp_pads.append(0)
temp_strides.append(1)
pads = temp_pads if op.pads is None else op.pads.get_value()
storage_order = 0 #if op.storage_order is None else op.storage_order.get_value()
strides = temp_strides if op.strides is None else op.strides.get_value(
)
if auto_pad != 'NOTSET':
out_shape = get_output_shape_pool(auto_pad, X.shape[2:],
kernel_shape, strides)
else:
input_spatial_shape = X.shape[2:]
out_shape = [0] * len(input_spatial_shape)
for i in range(len(input_spatial_shape)):
out_shape[i] = int(np.ceil(float(
input_spatial_shape[i] - \
(kernel_shape[i] - 1) + \
pads[i] + \
pads[i + len(kernel_shape)]
) / \
float(
strides[i]
)
)
)
Y_shape = np.zeros([X.shape[0], X.shape[1]] + list(out_shape),
dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if X.dtype == np.float32 else 'double'
attributes['AUTO_PAD'] = auto_pad
attributes['KERNEL_SHAPE'] = ", ".join(str(e) for e in kernel_shape)
attributes['PADS'] = ", ".join(str(e) for e in pads)
attributes['STORAGE_ORDER'] = storage_order
attributes['STRIDES'] = ", ".join(str(e) for e in strides)
opdesc = d5.compile_custom_cppop('MaxPool' + str(self.maxpool_counter),
MaxPoolCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
maxpoollayer = d5ref.custom_op(opdesc)
self.maxpool_counter += 1
self.add_node(op, network, maxpoollayer, self.maxpool_counter)
self.add_edges(op, network, self.maxpool_counter)
def visit_maxpool(self, op: d5.ops.MaxPool, network: ReferenceNetwork):
X = network.variables[op.i_X]
auto_pad = 'NOTSET' if op.auto_pad is None else op.auto_pad.get_value()
kernel_shape = op.kernel_shape.get_value()
temp_pads = []
temp_strides = []
for i in range(0, len(kernel_shape)):
temp_pads.append(0)
temp_pads.append(0)
temp_strides.append(1)
pads = temp_pads if op.pads is None else op.pads.get_value()
storage_order = 0 #if op.storage_order is None else op.storage_order.get_value()
strides = temp_strides if op.strides is None else op.strides.get_value()
if auto_pad != 'NOTSET':
out_shape = get_output_shape_pool(
auto_pad,
X.shape[2:],
kernel_shape,
strides
)
else:
input_spatial_shape = X.shape[2:]
out_shape = [0] * len(input_spatial_shape)
for i in range(len(input_spatial_shape)):
out_shape[i] = int(np.ceil(float(
input_spatial_shape[i] - \
(kernel_shape[i] - 1) + \
pads[i] + \
pads[i + len(kernel_shape)]
) / \
float(
strides[i]
)
)
)
Y_shape = np.zeros([X.shape[0], X.shape[1]] + list(out_shape), dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
maxpoollayer = MaxPoolOp(
input_desc,
output_desc,
auto_pad,
kernel_shape,
pads,
storage_order,
strides
)
self.maxpool_counter += 1
self.add_node(op, network, maxpoollayer, self.maxpool_counter)
self.add_edges(op, network, self.maxpool_counter)
def visit_relu(self, op: d5.ops.Relu, network: ReferenceNetwork):
X = network.variables[op.i_X]
Y_shape = np.zeros(X.shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
relulayer = ReluOp(input_desc, output_desc)
self.relu_counter += 1
self.add_node(op, network, relulayer, self.relu_counter)
self.add_edges(op, network, self.relu_counter)
def visit_mean_squared_error(self, op: d5.ops.MeanSquaredError,
network: ReferenceNetwork):
X, target = network.variables[op.i_X], network.variables[op.i_target]
input_desc = [desc_from_tensor(X), desc_from_tensor(target)]
L_shape = np.zeros(1, dtype=X.dtype)
output_desc = [desc_from_tensor(L_shape)]
network.variables[op.o_output] = L_shape
mselayer = MeanSquaredErrorOp(input_desc, output_desc)
self.loss_counter += 1
op_name = 'mean_squared_error (loss_op#%d)' % (self.loss_counter)
network.losses[op.o_output] = (mselayer, op, op_name)
def visit_softmax_cross_entropy(self, op: d5.ops.SoftmaxCrossEntropy,
network: ReferenceNetwork):
X, target = network.variables[op.i_X], network.variables[op.i_target]
input_desc = [desc_from_tensor(X), desc_from_tensor(target)]
L_shape = np.zeros(1, dtype=X.dtype)
output_desc = [desc_from_tensor(L_shape)]
network.variables[op.o_output] = L_shape
lcelayer = SoftmaxCrossEntropyOp(input_desc, output_desc)
self.loss_counter += 1
op_name = 'label_cross_entropy (loss_op#%d)' % (self.loss_counter)
network.losses[op.o_output] = (lcelayer, op, op_name)
def visit_softmax(self, op: d5.ops.Softmax, network: ReferenceNetwork):
softmax_input = network.variables[op.i_input]
axis = 1 if op.axis is None else op.axis.get_value()
Y_shape = np.zeros(softmax_input.shape, dtype=softmax_input.dtype)
network.variables[op.o_output] = Y_shape
network.gradients[op.i_input] = np.zeros_like(softmax_input)
input_desc = [desc_from_tensor(softmax_input)]
output_desc = [desc_from_tensor(Y_shape)]
softmaxlayer = SoftmaxOp(input_desc, output_desc, axis)
self.softmax_counter += 1
self.add_node(op, network, softmaxlayer, self.softmax_counter)
self.add_edges(op, network, self.softmax_counter)
def visit_add(self, op: d5.ops.Add, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
C = np.zeros((A + B).shape, dtype=A.dtype)
network.variables[op.o_C] = C
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
input_desc = [desc_from_tensor(A), desc_from_tensor(B)]
output_desc = [desc_from_tensor(C)]
addlayer = AddOp(input_desc, output_desc)
self.add_counter += 1
self.add_node(op, network, addlayer, self.add_counter)
self.add_edges(op, network, self.add_counter)
def visit_squeeze(self, op: d5.ops.Squeeze, network: ReferenceNetwork):
data = network.variables[op.i_data]
axes = op.axes.get_value()
squeezed = np.zeros(data.shape, dtype=data.dtype)
squeezed = np.squeeze(squeezed, axis=tuple(axes))
network.variables[op.o_squeezed] = squeezed
network.gradients[op.i_data] = np.zeros_like(data)
input_desc = [desc_from_tensor(data)]
output_desc = [desc_from_tensor(squeezed)]
squeezelayer = SqueezeOp(input_desc, output_desc, axes)
self.squeeze_counter += 1
self.add_node(op, network, squeezelayer, self.squeeze_counter)
self.add_edges(op, network, self.squeeze_counter)
def visit_unsqueeze(self, op: d5.ops.Unsqueeze, network: ReferenceNetwork):
data = network.variables[op.i_data]
axes = op.axes.get_value()
expanded = np.zeros(data.shape, dtype=data.dtype)
for i in range(len(axes)):
expanded = np.expand_dims(expanded, axis=axes[i])
network.variables[op.o_expanded] = expanded
network.gradients[op.i_data] = np.zeros_like(data)
input_desc = [desc_from_tensor(data)]
output_desc = [desc_from_tensor(expanded)]
unsqueezelayer = UnsqueezeOp(input_desc, output_desc, axes)
self.unsqueeze_counter += 1
self.add_node(op, network, unsqueezelayer, self.unsqueeze_counter)
self.add_edges(op, network, self.unsqueeze_counter)
def visit_globalaveragepool(self, op: d5.ops.GlobalAveragePool,
network: ReferenceNetwork):
X = network.variables[op.i_X]
out_shape = [X.shape[0], X.shape[1]]
for i in range(len(X.shape) - 2):
out_shape = out_shape + [1]
Y_shape = np.zeros(out_shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
globalaveragepoollayer = GlobalAveragePoolOp(input_desc, output_desc)
self.globalaveragepool_counter += 1
self.add_node(op, network, globalaveragepoollayer,
self.globalaveragepool_counter)
self.add_edges(op, network, self.globalaveragepool_counter)
def visit_sum(self, op: d5.ops.Sum, network: ReferenceNetwork):
inputs = []
for i in range(0, len(op.input)):
inputs.append(network.variables[op.input[i]])
network.gradients[op.input[i]] = np.zeros_like(
network.variables[op.input[i]])
Y_shape = np.copy(inputs[0])
for i in range(1, len(inputs)):
Y_shape = Y_shape + inputs[i]
network.variables[op.o_sum] = Y_shape
input_desc = [desc_from_tensor(i) for i in inputs]
output_desc = [desc_from_tensor(Y_shape)]
sumlayer = SumOp(input_desc, output_desc)
self.sum_counter += 1
self.add_node(op, network, sumlayer, self.sum_counter)
self.add_edges(op, network, self.sum_counter)
def visit_matmul(self, op: d5.ops.MatMul, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
Y_shape = np.zeros((np.dot(A, B)).shape, dtype=A.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
input_desc = [desc_from_tensor(i) for i in [A, B]]
output_desc = [desc_from_tensor(Y_shape)]
matmullayer = MatMulOp(
input_desc,
output_desc,
)
self.matmul_counter += 1
self.add_node(op, network, matmullayer, self.matmul_counter)
self.add_edges(op, network, self.matmul_counter)
def visit_reshape(self, op: d5.ops.Reshape, network: ReferenceNetwork):
data = network.variables[op.i_data]
shape = network.variables[op.i_shape]
#compute output shape
output_shape = np.ndarray(shape.shape, dtype=np.int64)
for i in range(0, shape.size):
if shape[i] == 0:
output_shape[i] = data.shape[i]
elif shape[i] == -1:
input_size = data.size
temp = 1
for j in range(0, i):
if shape[j] == 0:
temp *= data.shape[j]
else:
temp *= shape[j]
for j in range(i + 1, shape.size):
if shape[j] == 0:
temp *= data.shape[j]
else:
temp *= shape[j]
output_shape[i] = input_size / temp
else:
output_shape[i] = shape[i]
Y_shape = np.zeros(output_shape, dtype=data.dtype)
network.variables[op.o_reshaped] = Y_shape
network.gradients[op.i_data] = np.zeros_like(data)
network.gradients[op.i_shape] = np.zeros_like(shape)
input_desc = [desc_from_tensor(data), desc_from_tensor(shape)]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if data.dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('Reshape' + str(self.reshape_counter),
ReshapeCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
reshapelayer = d5ref.custom_op(opdesc)
self.reshape_counter += 1
self.add_node(op, network, reshapelayer, self.reshape_counter)
self.add_edges(op, network, self.reshape_counter)
def visit_relu(self, op: d5.ops.Relu, network: ReferenceNetwork):
X = network.variables[op.i_X]
Y_shape = np.zeros(X.shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
input_desc = [desc_from_tensor(X)]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if X.dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('Relu' + str(self.relu_counter),
ReluCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
relulayer = d5ref.custom_op(opdesc)
self.relu_counter += 1
self.add_node(op, network, relulayer, self.relu_counter)
self.add_edges(op, network, self.relu_counter)
def visit_reshape(self, op: d5.ops.Reshape, network: ReferenceNetwork):
data = network.variables[op.i_data]
shape = network.variables[op.i_shape]
#compute output shape
output_shape = np.ndarray(shape.shape, dtype=np.int64)
for i in range(0, shape.size):
if shape[i] == 0:
output_shape[i] = data.shape[i]
elif shape[i] == -1:
input_size = data.size
temp = 1
for j in range(0, i):
if shape[j] == 0:
temp *= data.shape[j]
else:
temp *= shape[j]
for j in range(i+1, shape.size):
if shape[j] == 0:
temp *= data.shape[j]
else:
temp *= shape[j]
output_shape[i] = input_size / temp
else:
output_shape[i] = shape[i]
Y_shape = np.zeros(output_shape, dtype=data.dtype)
network.variables[op.o_reshaped] = Y_shape
network.gradients[op.i_data] = np.zeros_like(data)
network.gradients[op.i_shape] = np.zeros_like(shape)
input_desc = [desc_from_tensor(data)]
output_desc = [desc_from_tensor(Y_shape)]
reshapelayer = ReshapeOp(
input_desc,
output_desc
)
self.reshape_counter += 1
self.add_node(op, network, reshapelayer, self.reshape_counter)
self.add_edges(op, network, self.reshape_counter)
def visit_matmul(self, op: d5.ops.MatMul, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
Y_shape = np.zeros((np.dot(A, B)).shape, dtype=A.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
input_desc = [desc_from_tensor(i) for i in [A, B]]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if A.dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('Matmul' + str(self.matmul_counter),
MatMulCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
matmullayer = d5ref.custom_op(opdesc)
self.matmul_counter += 1
self.add_node(op, network, matmullayer, self.matmul_counter)
self.add_edges(op, network, self.matmul_counter)
def visit_add(self, op: d5.ops.Add, network: ReferenceNetwork):
A = network.variables[op.i_A]
B = network.variables[op.i_B]
C = np.zeros((A + B).shape, dtype=A.dtype)
network.variables[op.o_C] = C
network.gradients[op.i_A] = np.zeros_like(A)
network.gradients[op.i_B] = np.zeros_like(B)
input_desc = [desc_from_tensor(A), desc_from_tensor(B)]
output_desc = [desc_from_tensor(C)]
attributes = {}
attributes['DTYPE'] = 'float' if A.dtype == np.float32 else 'double'
opdesc = d5.compile_custom_cppop('Add' + str(self.add_counter),
AddCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
addlayer = d5ref.custom_op(opdesc)
self.add_counter += 1
self.add_node(op, network, addlayer, self.add_counter)
self.add_edges(op, network, self.add_counter)
def visit_softmax(self, op: d5.ops.Softmax, network: ReferenceNetwork):
softmax_input = network.variables[op.i_input]
axis = 1 if op.axis is None else op.axis.get_value()
Y_shape = np.zeros(softmax_input.shape, dtype=softmax_input.dtype)
network.variables[op.o_output] = Y_shape
network.gradients[op.i_input] = np.zeros_like(softmax_input)
input_desc = [desc_from_tensor(softmax_input)]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes[
'DTYPE'] = 'float' if softmax_input.dtype == np.float32 else 'double'
attributes['AXIS'] = axis
opdesc = d5.compile_custom_cppop('Softmax' + str(self.softmax_counter),
SoftmaxCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
softmaxlayer = d5ref.custom_op(opdesc)
self.softmax_counter += 1
self.add_node(op, network, softmaxlayer, self.softmax_counter)
self.add_edges(op, network, self.softmax_counter)
def visit_batchnormalization(self, op: d5.ops.BatchNormalization,
network: ReferenceNetwork):
X = network.variables[op.i_X]
scale = network.variables[op.i_scale]
B = network.variables[op.i_B]
mean = network.variables[op.i_mean]
var = network.variables[op.i_var]
#mean_out = np.zeros(mean.shape, dtype=mean.dtype)
#var_out = np.zeros(var.shape, dtype=var.dtype)
#mean_out2 = np.zeros(mean.shape, dtype=mean.dtype)
#var_out2 = np.zeros(var.shape, dtype=var.dtype)
epsilon = 1e-5 if op.epsilon is None else op.epsilon.get_value()
momentum = 0.9 if op.momentum is None else op.momentum.get_value()
spatial = 1 if op.spatial is None else op.spatial.get_value()
Y_shape = np.zeros(X.shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_scale] = np.zeros_like(scale)
network.gradients[op.i_B] = np.zeros_like(B)
network.gradients[op.i_mean] = np.zeros_like(mean)
network.gradients[op.i_var] = np.zeros_like(var)
input_desc = [
desc_from_tensor(X),
desc_from_tensor(scale),
desc_from_tensor(B),
desc_from_tensor(mean),
desc_from_tensor(var)
]
output_desc = [
desc_from_tensor(Y_shape)
#desc_from_tensor(mean_out),
#desc_from_tensor(var_out),
#desc_from_tensor(mean_out2),
#desc_from_tensor(var_out2)
]
attributes = {}
attributes['DTYPE'] = 'float' if X.dtype == np.float32 else 'double'
attributes['EPSILON'] = epsilon
attributes['MOMENTUM'] = momentum
attributes['SPATIAL'] = spatial
opdesc = d5.compile_custom_cppop('BatchNormalization_Inference' +
str(self.batchnormalization_counter),
BatchNormalizationInferenceCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
batchnormlayer_inference = d5ref.custom_op(opdesc)
self.batchnormalization_counter += 1
self.add_node(op, network, batchnormlayer_inference,
self.batchnormalization_counter)
self.add_edges(op, network, self.batchnormalization_counter)
def visit_conv(self, op: d5.ops.Conv, network: ReferenceNetwork):
X = network.variables[op.i_X]
W = network.variables[op.i_W]
if op.i_B is None:
temp_B = np.zeros(W.shape[0], dtype=W.dtype)
B = temp_B if op.i_B is None else network.variables[op.i_B]
input_tensor = [X, W]
else:
B = network.variables[op.i_B]
input_tensor = [X, W, B]
network.gradients[op.i_B] = np.zeros_like(B)
#optional input
#temp_B = np.zeros(W.shape[0], dtype=W.dtype)
#B = temp_B if len(op.input) < 3 else self.tensors[op.input[2]]
auto_pad = 'NOTSET' if op.auto_pad is None else op.auto_pad.get_value()
kernel_shape = W.shape[
2:] if op.kernel_shape is None else op.kernel_shape.get_value()
#default values if not specified
temp_dilations = []
temp_pads = []
temp_strides = []
for i in range(0, len(kernel_shape)):
temp_dilations.append(1)
temp_pads.append(0)
temp_pads.append(0)
temp_strides.append(1)
dilations = temp_dilations if op.dilations is None else op.dilations.get_value(
)
group = 1 if op.group is None else op.group.get_value()
pads = temp_pads if op.pads is None else op.pads.get_value()
strides = temp_strides if op.strides is None else op.strides.get_value(
)
if auto_pad != 'NOTSET':
out_shape = get_output_shape_conv(auto_pad, X.shape[2:],
kernel_shape, dilations, strides)
else:
input_spatial_shape = X.shape[2:]
out_shape = [0] * len(input_spatial_shape)
for i in range(len(input_spatial_shape)):
out_shape[i] = int(
np.floor(
float(
input_spatial_shape[i] + \
pads[i] + \
pads[i + len(kernel_shape)] - \
(dilations[i] * (kernel_shape[i] - 1) + 1)
) / \
float(
strides[i]
)
) + 1
)
Y_shape = np.zeros([X.shape[0], W.shape[0]] + list(out_shape),
dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
network.gradients[op.i_W] = np.zeros_like(W)
input_desc = [desc_from_tensor(i) for i in input_tensor]
output_desc = [desc_from_tensor(Y_shape)]
attributes = {}
attributes['DTYPE'] = 'float' if X.dtype == np.float32 else 'double'
attributes['AUTO_PAD'] = auto_pad
attributes['DILATIONS'] = ", ".join(str(e) for e in dilations)
attributes['GROUP'] = group
attributes['KERNEL_SHAPE'] = ", ".join(str(e) for e in kernel_shape)
attributes['PADS'] = ", ".join(str(e) for e in pads)
attributes['STRIDES'] = ", ".join(str(e) for e in strides)
opdesc = d5.compile_custom_cppop('Conv' + str(self.conv_counter),
ConvCPPOp,
input_desc,
output_desc,
additional_definitions=attributes)
convlayer = d5ref.custom_op(opdesc)
self.conv_counter += 1
self.add_node(op, network, convlayer, self.conv_counter)
self.add_edges(op, network, self.conv_counter)
def visit_conv(self, op: d5.ops.Conv, network: ReferenceNetwork):
X = network.variables[op.i_X]
W = network.variables[op.i_W]
if op.i_B is None:
temp_B = np.zeros(W.shape[0], dtype=W.dtype)
B = temp_B if op.i_B is None else network.variables[op.i_B]
input_tensor = [X, W]
else:
B = network.variables[op.i_B]
input_tensor = [X, W, B]
network.gradients[op.i_B] = np.zeros_like(B)
#optional input
#temp_B = np.zeros(W.shape[0], dtype=W.dtype)
#B = temp_B if len(op.input) < 3 else self.tensors[op.input[2]]
auto_pad = 'NOTSET' if op.auto_pad is None else op.auto_pad.get_value()
kernel_shape = W.shape[2:] if op.kernel_shape is None else op.kernel_shape.get_value()
#default values if not specified
temp_dilations = []
temp_pads = []
temp_strides = []
for i in range(0, len(kernel_shape)):
temp_dilations.append(1)
temp_pads.append(0)
temp_pads.append(0)
temp_strides.append(1)
dilations = temp_dilations if op.dilations is None else op.dilations.get_value()
group = 1 if op.group is None else op.group.get_value()
pads = temp_pads if op.pads is None else op.pads.get_value()
strides = temp_strides if op.strides is None else op.strides.get_value()
if auto_pad != 'NOTSET':
out_shape = get_output_shape_conv(
auto_pad,
X.shape[2:],
kernel_shape,
dilations,
strides
)
else:
input_spatial_shape = X.shape[2:]
out_shape = [0] * len(input_spatial_shape)
for i in range(len(input_spatial_shape)):
out_shape[i] = int(
np.floor(
float(
input_spatial_shape[i] + \
pads[i] + \
pads[i + len(kernel_shape)] - \
(dilations[i] * (kernel_shape[i] - 1) + 1)
) / \
float(
strides[i]
)
) + 1
)
Y_shape = np.zeros([X.shape[0], W.shape[0]] + list(out_shape), dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
network.gradients[op.i_W] = np.zeros_like(W)
input_tensor = [X, W] if len(op.input) == 2 else [X, W, B]
input_desc = [desc_from_tensor(i) for i in input_tensor]
output_desc = [desc_from_tensor(Y_shape)]
convlayer = ConvOp(
input_desc,
output_desc,
auto_pad,
dilations,
group,
kernel_shape,
pads,
strides)
self.conv_counter += 1
self.add_node(op, network, convlayer, self.conv_counter)
self.add_edges(op, network, self.conv_counter)
def visit_batchnormalization(self, op: d5.ops.BatchNormalization, network: ReferenceNetwork):
X = network.variables[op.i_X]
scale = network.variables[op.i_scale]
B = network.variables[op.i_B]
mean = network.variables[op.i_mean]
var = network.variables[op.i_var]
epsilon = 1e-5 if op.epsilon is None else op.epsilon.get_value()
momentum = 0.9 if op.momentum is None else op.momentum.get_value()
spatial = 1 if op.spatial is None else op.spatial.get_value()
Y_shape = np.zeros(X.shape, dtype=X.dtype)
network.variables[op.o_Y] = Y_shape
network.gradients[op.i_X] = np.zeros_like(X)
network.gradients[op.i_scale] = np.zeros_like(scale)
network.gradients[op.i_B] = np.zeros_like(B)
network.gradients[op.i_mean] = np.zeros_like(mean)
network.gradients[op.i_var] = np.zeros_like(var)
input_desc = [
desc_from_tensor(X),
desc_from_tensor(scale),
desc_from_tensor(B),
desc_from_tensor(mean),
desc_from_tensor(var)]
if not network.train_mode:
output_desc = [desc_from_tensor(Y_shape)]
batchnormlayer = BatchNormalizationInferenceOp(
input_desc,
output_desc,
epsilon,
momentum,
spatial
)
else:
mean_shape1 = np.zeros_like(mean)
mean_shape2 = np.zeros_like(mean)
var_shape1 = np.zeros_like(var)
var_shape2 = np.zeros_like(var)
output_desc = [
desc_from_tensor(Y_shape),
desc_from_tensor(mean_shape1),
desc_from_tensor(var_shape1),
desc_from_tensor(mean_shape2),
desc_from_tensor(var_shape2)
]
batchnormlayer = BatchNormalizationTrainingOp(
input_desc,
output_desc,
epsilon,
momentum,
spatial
)
self.batchnormalization_counter += 1
self.add_node(op, network, batchnormlayer, self.batchnormalization_counter)
self.add_edges(op, network, self.batchnormalization_counter)
