def __init__(self, num_classes):
super(GooGLeNet, self).__init__()
self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.conv2 = Conv2dBlock(64, 64, kernel_size=1)
self.conv3 = Conv2dBlock(64, 192, kernel_size=3, padding=0)
self.maxpool2 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
self.mean = P.ReduceMean(keep_dims=True)
self.dropout = nn.Dropout(keep_prob=0.8)
self.flatten = nn.Flatten()
self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
bias_init=weight_variable())
def __init__(self, block, num_classes=100, batch_size=32):
super(ResNet, self).__init__()
self.batch_size = batch_size
self.num_classes = num_classes
self.conv1 = conv7x7(3, 64, stride=2, padding=0)
self.bn1 = bn_with_initialize(64)
self.relu = P.ReLU()
self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME")
self.layer1 = MakeLayer0(block,
in_channels=64,
out_channels=256,
stride=1)
self.layer2 = MakeLayer1(block,
in_channels=256,
out_channels=512,
stride=2)
self.layer3 = MakeLayer2(block,
in_channels=512,
out_channels=1024,
stride=2)
self.layer4 = MakeLayer3(block,
in_channels=1024,
out_channels=2048,
stride=2)
self.pool = P.ReduceMean(keep_dims=True)
self.squeeze = P.Squeeze(axis=(2, 3))
self.fc = fc_with_initialize(512 * block.expansion, num_classes)
def __init__(self, block, layer_nums, in_channels, out_channels, strides,
num_classes, damping, loss_scale, frequency):
super(ResNet, self).__init__()
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError(
"the length of layer_num, in_channels, out_channels list must be 4!"
)
self.conv1 = _conv7x7(3,
64,
stride=2,
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.bn1 = _bn(64)
self.relu = P.ReLU()
self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3],
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.end_point = _fc(out_channels[3],
num_classes,
damping=damping,
loss_scale=loss_scale,
frequency=frequency)
def __init__(self, num_classes=10):
super(DefinedNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, weight_init="zeros")
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=2, strides=2)
self.flatten = nn.Flatten()
self.fc = nn.Dense(int(56*56*64), num_classes)
def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode)
self.max_pool = P.MaxPool(ksize=self.kernel_size,
strides=self.stride,
padding=self.pad_mode)
self.max_pool_with_arg_max = P.MaxPoolWithArgmax(
ksize=self.kernel_size, strides=self.stride, padding=self.pad_mode)
self.is_tbe = context.get_context("device_target") == "Ascend"
def __init__(self):
super(Net, self).__init__()
self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
kernel_size=3,
strides=2)
self.x = Parameter(initializer(
'normal', [1, 64, 112, 112]), name='w')
self.add = P.TensorAdd()
def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
self.b1 = Conv2dBlock(in_channels, n1x1, kernel_size=1)
self.b2 = nn.SequentialCell([Conv2dBlock(in_channels, n3x3red, kernel_size=1),
Conv2dBlock(n3x3red, n3x3, kernel_size=3, padding=0)])
self.b3 = nn.SequentialCell([Conv2dBlock(in_channels, n5x5red, kernel_size=1),
Conv2dBlock(n5x5red, n5x5, kernel_size=3, padding=0)])
self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=1, padding="same")
self.b4 = Conv2dBlock(in_channels, pool_planes, kernel_size=1)
self.concat = P.Concat(axis=1)
def __init__(self, network):
super(CenterFaceWithNms, self).__init__()
self.centerface_network = network
self.config = ConfigCenterface()
# two type of maxpool self.maxpool2d = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='same')
self.maxpool2d = P.MaxPoolWithArgmax(kernel_size=3, strides=1, pad_mode='same')
self.topk = P.TopK(sorted=True)
self.reshape = P.Reshape()
self.print = P.Print()
self.test_batch = self.config.test_batch_size
self.k = self.config.K
def __init__(self, kernel_size=1, stride=1, pad_mode="VALID", padding=0):
max_pool = P.MaxPool(ksize=kernel_size,
strides=stride,
padding=pad_mode)
self.is_autodiff_backend = False
if self.is_autodiff_backend:
# At present, pad mode of max pool is not unified, so it is a temporarily avoided
pad_mode = validator.check_string('pad_mode', pad_mode.lower(),
['valid', 'same'])
max_pool = P.MaxPoolWithArgmax(window=kernel_size,
stride=stride,
pad_mode=pad_mode,
pad=padding)
super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, padding,
max_pool)
def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
super(MaxPool1d, self).__init__(kernel_size, stride, pad_mode)
validator.check_value_type('kernel_size', kernel_size, [int], self.cls_name)
validator.check_value_type('stride', stride, [int], self.cls_name)
self.pad_mode = validator.check_string(pad_mode.upper(), ['VALID', 'SAME'], 'pad_mode', self.cls_name)
validator.check_int(kernel_size, 1, Rel.GE, "kernel_size", self.cls_name)
validator.check_int(stride, 1, Rel.GE, "stride", self.cls_name)
self.kernel_size = (1, kernel_size)
self.stride = (1, stride)
self.max_pool = P.MaxPool(ksize=self.kernel_size,
strides=self.stride,
padding=self.pad_mode)
self.max_pool_with_arg_max = P.MaxPoolWithArgmax(ksize=self.kernel_size,
strides=self.stride,
padding=self.pad_mode)
self.shape = F.shape
self.reduce_mean = P.ReduceMean(keep_dims=True)
self.expand = P.ExpandDims()
self.squeeze = P.Squeeze(2)
self.is_tbe = context.get_context("device_target") == "Ascend"
'desc_inputs': [[3, 4, 6, 6], [3, 4, 3, 3], [3, 4, 3, 3]],
'desc_bprop': [[3, 4, 6, 6]],
'skip': ['backward']}),
('AvgPool', {
'block': P.AvgPool(ksize=(2, 2), strides=(2, 2), padding="VALID"),
'desc_inputs': [[100, 3, 28, 28]],
'desc_bprop': [[100, 3, 14, 14]]}),
('AvgPoolGrad', {
'block': G.AvgPoolGrad(ksize=(2, 2), strides=(2, 2), padding="VALID"),
'desc_const': [(3, 4, 6, 6)],
'const_first': True,
'desc_inputs': [[3, 4, 6, 6]],
'desc_bprop': [[3, 4, 6, 6]],
'skip': ['backward']}),
('MaxPoolWithArgmax', {
'block': P.MaxPoolWithArgmax(ksize=2, strides=2),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128, 32, 8, 16], [128, 32, 8, 16]]}),
('SoftmaxCrossEntropyWithLogits', {
'block': P.SoftmaxCrossEntropyWithLogits(),
'desc_inputs': [[1, 10], [1, 10]],
'desc_bprop': [[1], [1, 10]],
'skip': ['backward_exec']}),
('Flatten', {
'block': P.Flatten(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128 * 32 * 8 * 16]]}),
('LogSoftmax', {
'block': P.LogSoftmax(),
'desc_inputs': [[64, 2]],
'desc_bprop': [[160, 30522]]}),
def __init__(self):
super(Net, self).__init__()
self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
'skip': ['backward']
}),
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_2', {
'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_3', {
'block': (lambda _: P.MaxPoolWithArgmax(ksize=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
'skip': ['backward']
}),
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(pad_mode='sane'), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_2', {
'block': (lambda _: P.MaxPoolWithArgmax(kernel_size='1'), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_3', {
'block': (lambda _: P.MaxPoolWithArgmax(kernel_size=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
('ApplyMomentum_Error', {
'block': (P.ApplyMomentum(), {'exception': TypeError}),
'desc_inputs': [[2], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64]],
'desc_bprop': [[128, 32, 32, 64]],
'skip': ['backward']
}),
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': TypeError}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {'exception': ValueError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {'exception': ValueError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_2', {
'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': TypeError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_3', {
'block': (lambda _: P.MaxPoolWithArgmax(ksize=-2), {'exception': ValueError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_4', {
'block': (lambda _: P.MaxPoolWithArgmax(strides=-1), {'exception': ValueError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_1', {
def __init__(self):
super(Net, self).__init__()
self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
window=3,
stride=2)
'desc_inputs': [[3, 4, 6, 6], [3, 4, 3, 3], [3, 4, 3, 3]],
'desc_bprop': [[3, 4, 6, 6]],
'skip': ['backward']}),
('AvgPool', {
'block': P.AvgPool(ksize=(2, 2), strides=(2, 2), padding="VALID"),
'desc_inputs': [[100, 3, 28, 28]],
'desc_bprop': [[100, 3, 14, 14]]}),
('AvgPoolGrad', {
'block': G.AvgPoolGrad(ksize=(2, 2), strides=(2, 2), padding="VALID"),
'desc_const': [(3, 4, 6, 6)],
'const_first': True,
'desc_inputs': [[3, 4, 6, 6]],
'desc_bprop': [[3, 4, 6, 6]],
'skip': ['backward']}),
('MaxPoolWithArgmax', {
'block': P.MaxPoolWithArgmax(window=2, stride=2),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128, 32, 8, 16], [128, 32, 8, 16]]}),
('SoftmaxCrossEntropyWithLogits', {
'block': P.SoftmaxCrossEntropyWithLogits(),
'desc_inputs': [[1, 10], [1, 10]],
'desc_bprop': [[1], [1, 10]],
'skip': ['backward_exec']}),
('Flatten', {
'block': P.Flatten(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[128 * 32 * 8 * 16]]}),
('LogSoftmax', {
'block': P.LogSoftmax(),
'desc_inputs': [[64, 2]],
'desc_bprop': [[160, 30522]]}),
def __init__(self, padding, ksize, strides):
super(MaxPoolWithArgMax_Net, self).__init__()
self.maxpool_with_argmax = P.MaxPoolWithArgmax(padding=padding, ksize=ksize, strides=strides)
# kernel_size != w_shape[2:4]
('DepthwiseConv2dNative6', {
'block': (P.DepthwiseConv2dNative(2, (5, 5)), {
'exception': ValueError,
'error_keywords': ['DepthwiseConv2dNative']
}),
'desc_inputs': [
Tensor(np.ones([1, 1, 9, 9]).astype(np.float32)),
Tensor(np.ones([2, 1, 5, 6]).astype(np.float32))
],
'skip': ['backward']
}),
# input is scalar
('MaxPoolWithArgmax0', {
'block': (P.MaxPoolWithArgmax(), {
'exception': TypeError,
'error_keywords': ['MaxPoolWithArgmax']
}),
'desc_inputs': [5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('MaxPoolWithArgmax1', {
'block': (P.MaxPoolWithArgmax(), {
'exception': TypeError,
'error_keywords': ['MaxPoolWithArgmax']
}),
'desc_inputs': [Tensor(np.ones([5]).astype(np.bool_))],
'skip': ['backward']
}),
def __init__(self):
super(Net_Pool2, self).__init__()
self.maxpool_fun = P.MaxPoolWithArgmax(ksize=3,
strides=2,
padding="SAME")
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from mindspore.ops import Primitive
from mindspore.ops import operations as P
tuple_getitem = Primitive('tuple_getitem')
add = P.TensorAdd()
max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
make_tuple = Primitive('make_tuple')
transdata = Primitive("TransData")
Transpose = P.Transpose()
class FnDict:
def __init__(self):
self.fnDict = {}
def __call__(self, fn):
self.fnDict[fn.__name__] = fn
def __getitem__(self, name):
return self.fnDict[name]
def __init__(self):
super(Net, self).__init__()
self.maxpool = P.MaxPoolWithArgmax(pad_mode="same",
kernel_size=3,
strides=2)
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import mindspore.common.dtype as mstype
from mindspore.ops import Primitive
from mindspore.ops import operations as P
addn = P.AddN()
add = P.TensorAdd()
reshape = P.Reshape()
cast = P.Cast()
tuple_getitem = Primitive('tuple_getitem')
max_pool = P.MaxPoolWithArgmax(pad_mode="same", kernel_size=3, strides=2)
def test_addn_cast(x, y, z):
mysum = addn((x, y, z))
res = cast(mysum, mstype.float16)
return res
def test_addn_with_max_pool(x, y):
mysum = addn((x, y))
output = max_pool(mysum)
res = tuple_getitem(output, 0)
return res
def __init__(self):
""" ComparisonNet definition """
super(NetMaxPoolWithArgMax, self).__init__()
self.max_pool_with_arg_max = P.MaxPoolWithArgmax(padding="valid", ksize=2, strides=1)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from mindspore.ops import operations as P
from mindspore.ops import Primitive
import mindspore as ms
addn = P.AddN()
add = P.TensorAdd()
reshape = P.Reshape()
cast = P.Cast()
tuple_getitem = Primitive('tuple_getitem')
max_pool = P.MaxPoolWithArgmax(pad_mode="same", window=3, stride=2)
def test_addn_cast(x, y, z):
sum = addn((x, y))
res = cast(sum, ms.float16)
return res
def test_addn_with_max_pool(x, y):
sum = addn((x, y))
output = max_pool(sum)
res = tuple_getitem(output, 0)
return res
def test_shape_add(x1, x2, y1, y2, z1, z2):
sum1 = add(x1, x2)
test_cases_for_verify_exception = [
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(pad_mode='sane'), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_2', {
'block': (lambda _: P.MaxPoolWithArgmax(data_mode=2), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_3', {
'block': (lambda _: P.MaxPoolWithArgmax(ceil_mode=2), {
'exception': ValueError
}),
'desc_inputs': [0],
def __init__(self, block, layer_nums, in_channels, out_channels, strides,
num_classes, damping, loss_scale, frequency, batch_size,
resnet_d, init_new):
super(ResNetNoBN, self).__init__()
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError(
"the length of layer_num, in_channels, out_channels list must be 4!"
)
self.conv1 = _conv7x7(3,
64,
stride=2,
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size)
# self.bn1 = _bn(64)
self.relu = P.ReLU()
self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
out_channel=out_channels[0],
stride=strides[0],
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size,
resnet_d=resnet_d)
self.layer2 = self._make_layer(block,
layer_nums[1],
in_channel=in_channels[1],
out_channel=out_channels[1],
stride=strides[1],
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size,
resnet_d=resnet_d)
self.layer3 = self._make_layer(block,
layer_nums[2],
in_channel=in_channels[2],
out_channel=out_channels[2],
stride=strides[2],
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size,
resnet_d=resnet_d)
self.layer4 = self._make_layer(block,
layer_nums[3],
in_channel=in_channels[3],
out_channel=out_channels[3],
stride=strides[3],
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size,
resnet_d=resnet_d)
self.mean = P.ReduceMean(keep_dims=True)
self.flatten = nn.Flatten()
self.end_point = _fc(out_channels[3],
num_classes,
damping=damping,
loss_scale=loss_scale,
frequency=frequency,
batch_size=batch_size)
L = sum(layer_nums) # e.g., resnet101 has 33 residual branches
for name, cell in self.cells_and_names():
if isinstance(cell, ResidualBlockNoBN):
# cell.conv3.weight.default_input = initializer('zeros', cell.conv3.weight.default_input.shape()).to_tensor()
fixup_m = 3 # !!!
v = math.pow(L, -1 / (2 * fixup_m - 2))
cell.conv1.weight.default_input = Tensor(
cell.conv1.weight.default_input.asnumpy() * v,
cell.conv1.weight.default_input.dtype())
cell.conv2.weight.default_input = Tensor(
cell.conv2.weight.default_input.asnumpy() * v,
cell.conv2.weight.default_input.dtype())
if init_new:
cell.conv3.weight.default_input = Tensor(
cell.conv3.weight.default_input.asnumpy() * v,
cell.conv3.weight.default_input.dtype())
else:
cell.conv3.weight.default_input = initializer(
'zeros',
cell.conv3.weight.default_input.shape()).to_tensor()
elif isinstance(cell, ResidualBlockNoBN):
cell.conv2.weight.default_input = initializer(
'zeros',
cell.conv2.weight.default_input.shape()).to_tensor()
fixup_m = 2 # !!!
v = math.pow(L, -1 / (2 * fixup_m - 2))
cell.conv1.weight.default_input = Tensor(
cell.conv1.weight.default_input.asnumpy() * v,
cell.conv1.weight.default_input.dtype())