def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, weights, kernel, stride, padding, groups
super(tf_Conv, self).__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
assert isinstance(
k, int), "Convolution with multiple kernels are not allowed."
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
conv = keras.layers.Conv2D(
c2,
k,
s,
'SAME' if s == 1 else 'VALID',
use_bias=False,
kernel_initializer=keras.initializers.Constant(
w.conv.weight.permute(2, 3, 1, 0).numpy()))
self.conv = conv if s == 1 else keras.Sequential(
[tf_Pad(autopad(k, p)), conv])
self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity
# YOLOv5 activations
if isinstance(w.act, nn.LeakyReLU):
self.act = (lambda x: keras.activations.relu(x, alpha=0.1)
) if act else tf.identity
elif isinstance(w.act, nn.Hardswish):
self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
) if act else tf.identity
elif isinstance(w.act, nn.SiLU):
self.act = (
lambda x: keras.activations.swish(x)) if act else tf.identity
def __init__(
self: tf_Conv,
in_channels: int,
out_channels: int, # filters
kernel_size: int = 1,
strides: int = 1,
padding: Optional[int] = None,
groups: int = 1,
activate: bool = True,
module: Optional[nn.Module] = None
) -> None:
super().__init__()
assert module is not None
# TF v2.2 Conv2D does not support 'groups' argument"
assert groups == 1
# Convolute multi kernels are not allowed
assert isinstance(kernel_size, int)
# Convolution
# TensorFlow convolution padding is inconsistent
# with PyTorch (e.g. k=3 s=2 'SAME' padding)
# see https://stackoverflow.com/questions/52975843
# /comparing-conv2d-with-padding-between-tensorflow-and-pytorch
if strides == 1:
padding_tf = 'SAME'
else:
padding_tf = 'VALID'
kernel_initializer = Constant(
module.conv.weight.permute(2, 3, 1, 0).numpy()
)
conv = Conv2D(
filters=out_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding_tf,
use_bias=False,
kernel_initializer=kernel_initializer
)
if strides == 1:
self.conv = conv
else:
self.conv = Sequential([
tf_Pad(autopad(kernel_size, padding)),
conv
])
# batch normalization
if hasattr(module, 'bn'):
self.bn = tf_BN(module=module.bn)
else:
self.bn = tf.indentity
# activation
if not activate:
self.act = tf.identity
elif isinstance(module.act, nn.LeakyReLU):
self.act = (lambda x: relu(x, alpha=0.1))
elif isinstance(module.act, nn.Hardswish):
self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667)
elif isinstance(module.act, nn.SiLU):
# self.act = (lambda x: x * keras.activations.sigmoid(x))
self.act = (lambda x: swish(x))
return
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
# ch_in, ch_out, weights, kernel, stride, padding, groups
super().__init__()
assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels'
conv = keras.layers.DepthwiseConv2D(
kernel_size=k,
depth_multiplier=c2 // c1,
strides=s,
padding='SAME' if s == 1 else 'VALID',
use_bias=not hasattr(w, 'bn'),
depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
self.act = activations(w.act) if act else tf.identity
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, weights, kernel, stride, padding, groups
super().__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
conv = keras.layers.Conv2D(
filters=c2,
kernel_size=k,
strides=s,
padding='SAME' if s == 1 else 'VALID',
use_bias=not hasattr(w, 'bn'),
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
self.act = activations(w.act) if act else tf.identity