I build my own vgg network
then I create the dataset then run the train
def run_train(dataset, num_epochs=2):
start_time = time.perf_counter()
model = VGGBase()
for _ in tf.data.Dataset.range(num_epochs):
for image,target in dataset: # (batch_size (N), 300, 300, 3)
print(type(image), type(target))
this type is
<class 'tensorflow.python.framework.ops.EagerTensor'> <class 'tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor'>
Then I got the following error on
File "/home/jake/Gits/ssd_tensorflow/model.py", line 56, in call x = self.conv1_1(x)# (N, 64, 300, 300) TypeError: '_TupleWrapper' object is not callable
class VGGBase(Model): def init(self): super(VGGBase,self).init() self.padding_1 = tf.keras.layers.ZeroPadding2D(padding=(1, 1)) # put this before your conv layer self.conv1_1 = tf.keras.layers.Conv2D(3, kernel_size=3,padding='same',strides=1, activation='relu'), self.conv1_2 = tf.keras.layers.Conv2D(64, kernel_size=3, padding='same',strides=1,activation='relu'), self.pool1 = tf.keras.layers.MaxPool2D(2,2),
self.conv2_1 = tf.keras.layers.Conv2D(128, kernel_size=3, padding='same',strides= 1,activation='relu'), self.conv2_2 = tf.keras.layers.Conv2D(128, kernel_size=3,padding='same',strides= 1,activation='relu'), self.pool2 = tf.keras.layers.MaxPool2D(2,2), self.conv3_1 = tf.keras.layers.Conv2D(256, kernel_size=3, padding='same',strides= 1,activation='relu'), self.conv3_2 = tf.keras.layers.Conv2D(256, kernel_size=3, padding='same',strides= 1,activation='relu'), self.conv3_3 = tf.keras.layers.Conv2D(256, kernel_size=3, padding='same',strides= 1,activation='relu'), self.pool3 = tf.keras.layers.MaxPool2D(2,2), self.conv4_1 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.conv4_2 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.conv4_3 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.pool4 = tf.keras.layers.MaxPool2D(2, 2), self.conv5_1 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.conv5_2 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.conv5_3 = tf.keras.layers.Conv2D(512, kernel_size=3, padding='same', strides=1,activation='relu'), self.pool5 = tf.keras.layers.MaxPool2D(2, 2), self.padding6 = tf.keras.layers.ZeroPadding2D(padding=(6, 6)) # put this before your conv layer self.conv6 = tf.keras.layers.Conv2D(1024, kernel_size=3, padding='same',dilation_rate=6,activation='relu') # atrous convolution self.conv7 = tf.keras.layers.Conv2D(1024, kernel_size=1,activation='relu') #self.load_weights() def call(self,x): x = self.padding_1(x) x = self.conv1_1(x)# (N, 64, 300, 300) x = self.conv1_2(x)# (N, 64, 300, 300) x = self.pool1(x) # (N, 64, 150, 150) x = self.conv2_1(x) # (N, 128, 150, 150) x = self.conv2_2(x) # (N, 128, 150, 150) x = self.pool2(x)# (N, 128, 75, 75) x = self.conv3_1(x) # (N, 256, 75, 75) x = self.conv3_2(x)# (N, 256, 75, 75) x = self.conv3_3(x)# (N, 256, 75, 75) x = self.pool3(x) #(N, 256, 38, 38), it would have been 37 if not for ceil_mode = True x = self.conv4_1(x)# (N, 512, 38, 38) x = self.conv4_2(x)# (N, 512, 38, 38) x = self.conv4_3(x)# (N, 512, 38, 38) conv4_3_feats = x# (N, 512, 38, 38) x = self.pool4(x)# (N, 512, 19, 19) x = self.conv5_1(x) # (N, 512, 19, 19) x = self.conv5_2(x) # (N, 512, 19, 19) x = self.conv5_3(x) # (N, 512, 19, 19) x = self.pool5(x) # (N, 512, 19, 19), pool5 does not reduce dimensions x = self.padding6(x) x = self.conv6(x) # (N, 1024, 19, 19) x = self.conv7(x) # (N, 1024, 19, 19) conv7_feats = x return conv4_3_feats, conv7_feats