def __init__(self):
self.layers = [
# before the first convblock:
ConvLayer((7, 7), c_in=3, c_out=64, stride=2, padding='SAME'),
BatchNormLayer(64),
ReLULayer(),
MaxPoolLayer(dim=3),
# convblock:
ConvBlock(c_in=64, fm_sizes=[64, 64, 256], stride=1),
# identity block x 2:
IdentityBlock(c_in=256, fm_sizes=[64, 64, 256]),
IdentityBlock(c_in=256, fm_sizes=[64, 64, 256]),
# convblock:
ConvBlock(c_in=256, fm_sizes=[128, 128, 512], stride=2),
# identity block x 3:
IdentityBlock(c_in=512, fm_sizes=[128, 128, 512]),
IdentityBlock(c_in=512, fm_sizes=[128, 128, 512]),
IdentityBlock(c_in=512, fm_sizes=[128, 128, 512]),
# convblock:
ConvBlock(c_in=512, fm_sizes=[256, 256, 1024], stride=2),
# identity block x 5:
IdentityBlock(c_in=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(c_in=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(c_in=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(c_in=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(c_in=1024, fm_sizes=[256, 256, 1024]),
# convblock:
ConvBlock(c_in=1024, fm_sizes=[512, 512, 2048], stride=2),
# identity block x 2:
IdentityBlock(c_in=2048, fm_sizes=[512, 512, 2048]),
IdentityBlock(c_in=2048, fm_sizes=[512, 512, 2048]),
# pool / flatten / dense:
AvgPool(kernel_size=7),
Flatten(),
DenseLayer(m_in=2048, m_out=1000)
]
self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))
self.output = self.forward(self.input_)
def __init__(self):
self.layers = [
# before conv block
ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'),
BatchNormLayer(64),
ReLULayer(),
MaxPoolLayer(dim=3),
# conv block
ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1),
]
self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3))
self.output = self.forward(self.input_)
def __init__(self):
self.layers = [
# before conv block
ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'),
BatchNormLayer(64),
ReLULayer(),
MaxPoolLayer(dim=3),
# conv block
ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1),
]
self.input_ = keras.Input(shape=(224, 224, 3), dtype=tf.float32)
self.createModel_(self.input_)
def __init__(self):
self.layers = [
# before conv block
ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'),
BatchNormLayer(64),
ReLULayer(),
MaxPoolLayer(dim=3),
# conv block
ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1),
# identity block x 2
IdentityBlock(mi=256, fm_sizes=[64, 64, 256]),
IdentityBlock(mi=256, fm_sizes=[64, 64, 256]),
# conv block
ConvBlock(mi=256, fm_sizes=[128, 128, 512], stride=2),
# identity block x 3
IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),
IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),
IdentityBlock(mi=512, fm_sizes=[128, 128, 512]),
# conv block
ConvBlock(mi=512, fm_sizes=[256, 256, 1024], stride=2),
# identity block x 5
IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),
IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]),
# conv block
ConvBlock(mi=1024, fm_sizes=[512, 512, 2048], stride=2),
# identity block x 2
IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]),
IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]),
# pool / flatten / dense
AvgPool(ksize=7),
Flatten(),
DenseLayer(mi=2048, mo=1000)
]
self.input_ = tf.compat.v1.placeholder(tf.float32,
shape=(None, 224, 224, 3))
self.output = self.forward(self.input_)
def __init__(self): self.layers = [ # before ConvBlock: ConvLayer((7, 7), c_in=3, c_out=64, stride=2, padding='SAME'), BatchNormLayer(64), ReLULayer(), MaxPoolLayer(dim=3), # ConvBlock: ConvBlock(c_in=64, fm_sizes=[64, 64, 256], stride=1), ] self.input_ = tf.placeholder(tf.float32, shape=[None, 224, 224, 3]) self.output = self.forward(self.input_)