def __init__(self,
img_rows=224,
img_cols=224,
weights="imagenet",
classes=1000,
**kwargs):
super(ResNet50, self).__init__(use_queue=False, **kwargs)
self.img_cols = img_cols
self.img_rows = img_rows
self.weights = weights
self.classes = classes
input = Feature(shape=(None, self.img_rows, self.img_cols, 3))
labels = Label(shape=(None, self.classes))
conv1 = Conv2D(num_outputs=64,
kernel_size=7,
stride=2,
activation='linear',
padding='same',
in_layers=[input])
bn1 = BatchNorm(in_layers=[conv1])
ac1 = ReLU(bn1)
pool1 = MaxPool2D(ksize=[1, 3, 3, 1], in_layers=[bn1])
cb1 = self.conv_block(pool1, 3, [64, 64, 256], 1)
id1 = self.identity_block(cb1, 3, [64, 64, 256])
id1 = self.identity_block(id1, 3, [64, 64, 256])
cb2 = self.conv_block(id1, 3, [128, 128, 512])
id2 = self.identity_block(cb2, 3, [128, 128, 512])
id2 = self.identity_block(id2, 3, [128, 128, 512])
id2 = self.identity_block(id2, 3, [128, 128, 512])
cb3 = self.conv_block(id2, 3, [256, 256, 1024])
id3 = self.identity_block(cb3, 3, [256, 256, 1024])
id3 = self.identity_block(id3, 3, [256, 256, 1024])
id3 = self.identity_block(id3, 3, [256, 256, 1024])
id3 = self.identity_block(cb3, 3, [256, 256, 1024])
id3 = self.identity_block(id3, 3, [256, 256, 1024])
cb4 = self.conv_block(id3, 3, [512, 512, 2048])
id4 = self.identity_block(cb4, 3, [512, 512, 2048])
id4 = self.identity_block(id4, 3, [512, 512, 2048])
pool2 = AvgPool2D(ksize=[1, 7, 7, 1], in_layers=[id4])
flatten = Flatten(in_layers=[pool2])
dense = Dense(classes, in_layers=[flatten])
loss = SoftMaxCrossEntropy(in_layers=[labels, dense])
loss = ReduceMean(in_layers=[loss])
self.set_loss(loss)
self.add_output(dense)
def test_avgpool2D(self):
"""Test that AvgPool2D can be invoked."""
length = 4
width = 4
in_channels = 5
batch_size = 10
in_tensor = np.random.rand(batch_size, length, width, in_channels)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = AvgPool2D()(in_tensor)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, 2, 2, in_channels)