def AlexNet(weights_path=None, heatmap=False):
if heatmap:
inputs = Input(shape=(3,None,None))
# inputs = Input(shape=(None, None, 3))
else:
inputs = Input(shape=(3, 227, 227))
# inputs = Input(shape=(227, 227, 3))
conv_1 = Conv2D(96, 11, 11,strides=(4,4),activation='relu',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2,2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2,2))(conv_2)
conv_2 = merge([
Conv2D(128,5,5,activation="relu",name='conv_2_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_2)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1,1))(conv_3)
conv_3 = Conv2D(384,3,3,activation='relu',name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1,1))(conv_3)
conv_4 = merge([
Conv2D(192,3,3,activation="relu",name='conv_4_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_4)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_4")
conv_5 = ZeroPadding2D((1,1))(conv_4)
conv_5 = merge([
Conv2D(128,3,3,activation="relu",name='conv_5_'+str(i+1))(
splittensor(ratio_split=2,id_split=i)(conv_5)
) for i in range(2)], mode='concat',concat_axis=1,name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2,2),name="convpool_5")(conv_5)
if heatmap:
dense_1 = Conv2D(4096,6,6,activation="relu",name="dense_1")(dense_1)
dense_2 = Conv2D(4096,1,1,activation="relu",name="dense_2")(dense_1)
dense_3 = Conv2D(1000, 1,1,name="dense_3")(dense_2)
prediction = Softmax4D(axis=1,name="softmax")(dense_3)
else:
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096, activation='relu',name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu',name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(1000,name='dense_3')(dense_3)
prediction = Activation("softmax",name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def VGG_16(weights_path=None, heatmap=False):
model = Sequential()
if heatmap:
model.add(ZeroPadding2D((1, 1), input_shape=(3, None, None)))
else:
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv4_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu', name='conv5_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
if heatmap:
model.add(Convolution2D(4096, 7, 7, activation="relu", name="dense_1"))
model.add(Convolution2D(4096, 1, 1, activation="relu", name="dense_2"))
model.add(Convolution2D(1000, 1, 1, name="dense_3"))
model.add(Softmax4D(axis=1, name="softmax"))
else:
model.add(Flatten(name="flatten"))
model.add(Dense(4096, activation='relu', name='dense_1'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu', name='dense_2'))
model.add(Dropout(0.5))
model.add(Dense(1000, name='dense_3'))
model.add(Activation("softmax", name="softmax"))
if weights_path:
model.load_weights(weights_path)
return model
def AlexNet(weights_path=None, heatmap=False):
if heatmap:
inputs = Input(shape=(3, None, None))
else:
inputs = Input(shape=(3, 227, 227))
conv_1 = Convolution2D(96, kernel_size=(11, 11), subsample=(4, 4), activation='relu', data_format='channels_first',
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name='convpool_1')(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(128, kernel_size=(5, 5), activation='relu', data_format='channels_first', name='conv_2_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_2)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_2')
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv_3 = Convolution2D(384, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_3')(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv_4 = merge([
Convolution2D(192, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_4_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_4)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_4')
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv_5 = merge([
Convolution2D(128, kernel_size=(3, 3), activation='relu', data_format='channels_first', name='conv_5_' + str(i + 1))(
splittensor(ratio_split=2, id_split=i)(conv_5)
) for i in range(2)], mode='concat', concat_axis=1, name='conv_5')
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name='convpool_5')(conv_5)
if heatmap:
dense_1 = Convolution2D(4096, kernel_size=(6, 6), activation='relu', data_format='channels_first', name='dense_1')(dense_1)
dense_2 = Convolution2D(4096, kernel_size=(1, 1), activation='relu', data_format='channels_first', name='dense_2')(dense_1)
dense_3 = Convolution2D(1000, kernel_size=(1, 1), data_format='channels_first', name='dense_3')(dense_2)
prediction = Softmax4D(axis=1, name='softmax')(dense_3)
else:
dense_1 = Flatten(name='flatten')(dense_1)
dense_1 = Dense(4096, activation='relu', name='dense_1')(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096, activation='relu', name='dense_2')(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(1000, name='dense_3')(dense_3)
prediction = Activation('softmax', name='softmax')(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def AlexNet(outdim=1000, weights_path=None, heatmap=False, l1=0, l2=0, usemil=False, usemymil=False, k=1., usemysoftmil=False, softmink=1., softmaxk=1.,\
sparsemil=False, sparsemill1=0., sparsemill2=0., saveact=False):
l1factor = l1
l2factor = l2
if heatmap:
inputs = Input(shape=(3, None, None))
else:
inputs = Input(shape=(3, 227, 227))
conv_1 = Convolution2D(96,
11,
11,
subsample=(4, 4),
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv_1')(inputs)
conv_2 = MaxPooling2D((3, 3), strides=(2, 2))(conv_1)
conv_2 = crosschannelnormalization(name="convpool_1")(conv_2)
conv_2 = ZeroPadding2D((2, 2))(conv_2)
conv_2 = merge([
Convolution2D(128,
5,
5,
activation="relu",
name='conv_2_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_2))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_2")
conv_3 = MaxPooling2D((3, 3), strides=(2, 2))(conv_2)
conv_3 = crosschannelnormalization()(conv_3)
conv_3 = ZeroPadding2D((1, 1))(conv_3)
conv_3 = Convolution2D(384,
3,
3,
activation='relu',
name='conv_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(conv_3)
conv_4 = ZeroPadding2D((1, 1))(conv_3)
conv_4 = merge([
Convolution2D(192,
3,
3,
activation="relu",
name='conv_4_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_4))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_4")
conv_5 = ZeroPadding2D((1, 1))(conv_4)
conv_5 = merge([
Convolution2D(128,
3,
3,
activation="relu",
name='conv_5_' + str(i + 1),
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(
splittensor(ratio_split=2, id_split=i)(conv_5))
for i in range(2)
],
mode='concat',
concat_axis=1,
name="conv_5")
dense_1 = MaxPooling2D((3, 3), strides=(2, 2), name="convpool_5")(conv_5)
if heatmap:
dense_1 = Convolution2D(4096,
6,
6,
activation="relu",
name="dense_1",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(4096,
1,
1,
activation="relu",
name="dense_2",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(outdim,
1,
1,
name="dense_3",
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
prediction = Softmax4D(axis=1, name="softmax")(dense_3)
elif usemil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(outdim,
1,
1,
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = Flatten(name='flatten')(prediction_1)
#dense_3 = Dense(outdim,name='dense_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor))(prediction)
#prediction = Activation("softmax",name="softmax2")(dense_3)
prediction_1 = MaxPooling2D((6, 6), name='output')(prediction_1)
prediction = Flatten(name='flatten')(prediction_1)
prediction = Recalc(axis=1, name='Recalcmil')(prediction)
elif usemymil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(1,
1,
1,
activation='sigmoid',
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = ExtractDim(axis=1, name='extract')(prediction_1)
prediction = Flatten(name='flatten')(dense_3)
prediction = ReRank(k=k, label=1, name='output')(prediction)
elif usemysoftmil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(1,
1,
1,
activation='sigmoid',
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction = ExtractDim(axis=1, name='extract')(prediction_1)
prediction = Flatten(name='flatten')(dense_3)
prediction = SoftReRank(softmink=softmink,
softmaxk=softmaxk,
label=1,
name='output')(prediction)
elif sparsemil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
prediction_1 = Convolution2D(1,1,1,activation='sigmoid',name='mil_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor),\
activity_regularizer=activity_l1l2(l1=sparsemill1, l2=sparsemill2))(dense_2)
# prediction_1 = Softmax4D(axis=1, name='softmax')(prediction_1)
#dense_3 = Convolution2D(outdim,1,1,name='mil_3',W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
#prediction_1 = ActivityRegularizerOneDim(l1=sparsemill1, l2=sparsemill2)(prediction_1)
#prediction = MaxPooling2D((6,6), name='output')(prediction_1)
# prediction_1 = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same', name='smooth', \
# W_regularizer=l1l2(l1=l1factor, l2=l2factor), activity_regularizer=activity_l1l2(l1=sparsemill1, l2=sparsemill2))(prediction_1)
prediction = Flatten(name='flatten')(prediction_1)
if saveact:
model = Model(input=inputs, output=prediction)
return model
prediction = RecalcExpand(axis=1, name='Recalcmil')(prediction)
else:
dense_1 = Flatten(name="flatten")(dense_1)
dense_1 = Dense(4096,
activation='relu',
name='dense_1',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_1)
dense_2 = Dropout(0.5)(dense_1)
dense_2 = Dense(4096,
activation='relu',
name='dense_2',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_2)
dense_3 = Dropout(0.5)(dense_2)
dense_3 = Dense(outdim,
name='dense_3',
W_regularizer=l1l2(l1=l1factor, l2=l2factor))(dense_3)
prediction = Activation("softmax", name="softmax")(dense_3)
model = Model(input=inputs, output=prediction)
if weights_path:
model.load_weights(weights_path)
return model
def VGG_19(outdim=1000,
weights_path=None,
heatmap=False,
l1=0,
l2=0,
usemil=False,
usemymil=False,
k=1.):
model = Sequential()
if heatmap:
model.add(ZeroPadding2D((1, 1), input_shape=(3, None, None)))
else:
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(
Convolution2D(64,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(64,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(128,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv2_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(128,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv3_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv3_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv3_3'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(256,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv3_4'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv4_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv4_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv4_3'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv4_4'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv5_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv5_2'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv5_3'))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(512,
3,
3,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='conv5_4'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
if heatmap:
model.add(
Convolution2D(4096,
7,
7,
activation="relu",
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name="dense_1"))
model.add(
Convolution2D(4096,
1,
1,
activation="relu",
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name="dense_2"))
model.add(
Convolution2D(outdim,
1,
1,
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name="dense_3"))
model.add(Softmax4D(axis=1, name="softmax"))
elif usemil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(outdim,
1,
1,
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
prediction = Flatten(name='flatten')(prediction_1)
dense_3 = Dense(outdim,
name='dense_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(prediction)
prediction = Activation("softmax", name="softmax2")(dense_3)
#prediction = MaxPooling2D((6,6), name='output')(prediction_1)
#prediction = Flatten(name='flatten')(prediction)
prediction = RecalcExpand(axis=1, name='Recalcmil')(prediction)
elif usemymil:
dense_1 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_1',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_2 = Convolution2D(128,
1,
1,
activation='relu',
name='mil_2',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_1)
dense_3 = Convolution2D(1,
1,
1,
activation='sigmoid',
name='mil_3',
W_regularizer=l1l2(l1=l1factor,
l2=l2factor))(dense_2)
#prediction_1 = Softmax4D(axis=1, name='softmax')(dense_3)
prediction = Flatten(name='flatten')(dense_3)
prediction = ReRank(k=k, label=1, name='output')(prediction)
else:
model.add(Flatten())
model.add(
Dense(4096,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='dense_1'))
model.add(Dropout(0.5))
model.add(
Dense(4096,
activation='relu',
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='dense_2'))
model.add(Dropout(0.5))
model.add(
Dense(outdim,
W_regularizer=l1l2(l1=l1factor, l2=l2factor),
name='dense_3'))
model.add(Activation("softmax"))
if weights_path:
model.load_weights(weights_path)
return model
def _emptySequential(layer_dim=[], heatmap=False):
default_layer_dim = [
1000, 4096, 4096, 512, 512, 512, 512, 512, 512, 256, 256, 256, 128,
128, 64, 64
]
layer_dim = layer_dim + default_layer_dim[len(layer_dim):]
model = Sequential()
if heatmap:
model.add(ZeroPadding2D((1, 1), input_shape=(3, None, None)))
else:
model.add(ZeroPadding2D((1, 1), input_shape=(3, 224, 224)))
model.add(
Convolution2D(layer_dim[-1],
3,
3,
activation='relu',
name=VGG16Layers.conv1a))
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-2],
3,
3,
activation='relu',
name=VGG16Layers.conv1b)) # H' = W' = 224
model.add(MaxPooling2D((2, 2), strides=(2, 2), name=VGG16Layers.pool1))
# W' = 112
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-3],
3,
3,
activation='relu',
name=VGG16Layers.conv2a)) # W' = 112
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-4],
3,
3,
activation='relu',
name=VGG16Layers.conv2b)) # W' = 112
model.add(MaxPooling2D((2, 2), strides=(2, 2), name=VGG16Layers.pool2))
# W' = 56
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-5],
3,
3,
activation='relu',
name=VGG16Layers.conv3a)) # W' = 56
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-6],
3,
3,
activation='relu',
name=VGG16Layers.conv3b)) # W' = 56
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-7],
3,
3,
activation='relu',
name=VGG16Layers.conv3c)) # W' = 56
model.add(MaxPooling2D((2, 2), strides=(2, 2), name=VGG16Layers.pool3))
# W' = 28
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-8],
3,
3,
activation='relu',
name=VGG16Layers.conv4a)) # W' = 28
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-9],
3,
3,
activation='relu',
name=VGG16Layers.conv4b)) # W' = 28
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-10],
3,
3,
activation='relu',
name=VGG16Layers.conv4c)) # W' = 28
model.add(MaxPooling2D((2, 2), strides=(2, 2), name=VGG16Layers.pool4))
# W' = 14
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-11],
3,
3,
activation='relu',
name=VGG16Layers.conv5a)) # W' = 14
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-12],
3,
3,
activation='relu',
name=VGG16Layers.conv5b)) # W' = 14
model.add(ZeroPadding2D((1, 1)))
model.add(
Convolution2D(layer_dim[-13],
3,
3,
activation='relu',
name=VGG16Layers.conv5c)) # W' = 14
model.add(MaxPooling2D((2, 2), strides=(2, 2), name=VGG16Layers.pool5))
# W' = 7
if heatmap:
model.add(
Convolution2D(layer_dim[-14],
7,
7,
activation="relu",
name=VGG16Layers.dense1))
model.add(
Convolution2D(layer_dim[-15],
1,
1,
activation="relu",
name=VGG16Layers.dense2))
model.add(
Convolution2D(layer_dim[-16], 1, 1, name=VGG16Layers.dense3))
model.add(Softmax4D(axis=1, name=VGG16Layers.softmax))
else:
# in: 512x49x1
model.add(Flatten(name="flatten"))
# out: 25088
model.add(
Dense(layer_dim[-14],
activation='relu',
name=VGG16Layers.dense1))
model.add(Dropout(0.5, name=VGG16Layers.drop1))
# fcd = fullyconnected-dropout # Dropout is applied to Dropout input -> we are dropping out one of the 4096 neurons
# out: 4096x1x1 (512x49x4096 weights)
# in: 4096x1x1
model.add(
Dense(layer_dim[-15],
activation='relu',
name=VGG16Layers.dense2))
model.add(Dropout(0.5, name=VGG16Layers.drop2))
# Dropout is applied to Dropout input -> we are dropping out one of the 4096 neurons
# out: out: 4096x1x1 (4096x4096 weights ~=16M)
# in: 4096x1x1
model.add(Dense(layer_dim[-16], name=VGG16Layers.dense3))
model.add(Activation("softmax", name=VGG16Layers.softmax))
# out: 1000x1x1 (1000x4096 weights ~= 4M)
return model