def testAllArchitectures(self):
for architecture, endpoint in DEFAULT_ENDPOINTS.items():
self.config.architecture = architecture
self.config.endpoint = endpoint
model = TruncatedBaseNetwork(self.config)
image = tf.placeholder(tf.float32, [1, 320, 320, 3])
# This should not fail.
out = model(image)
self.assertEqual(out.get_shape()[:3], (1, 20, 20))
# Free up memory for travis
tf.reset_default_graph()
gc.collect(generation=2)
def testTrainableVariables(self):
inputs = tf.placeholder(tf.float32, [1, 224, 224, 3])
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_3/bottleneck_v1/conv2',
'freeze_tail': False,
'use_tail': True,
}))
model(inputs)
# Variables in ResNet-50:
# (the order of beta and gamma depends on the TensorFlow's version)
# 0 conv1/weights:0
# 1 conv1/BatchNorm/(beta|gamma):0
# 2 conv1/BatchNorm/(beta|gamma):0
# 3 block1/unit_1/bottleneck_v1/shortcut/weights:0
# (...)
# 153 block4/unit_3/bottleneck_v1/conv2/weights:0
# 154 block4/unit_3/bottleneck_v1/conv2/BatchNorm/(beta|gamma):0
# 155 block4/unit_3/bottleneck_v1/conv2/BatchNorm/(beta|gamma):0
# --- endpoint ---
# 156 block4/unit_3/bottleneck_v1/conv3/weights:0
# 157 block4/unit_3/bottleneck_v1/conv3/BatchNorm/(beta|gamma):0
# 158 block4/unit_3/bottleneck_v1/conv3/BatchNorm/(beta|gamma):0
# 159 logits/weights:0
# 160 logits/biases:0
trainable_vars = model.get_trainable_vars()
self.assertEqual(len(trainable_vars), 156)
self.assertEqual(
trainable_vars[-3].name, 'truncated_base_network/resnet_v1_50/' +
'block4/unit_3/bottleneck_v1/conv2/weights:0')
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv2',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv1',
'freeze_tail': False,
'use_tail': True,
}))
model(inputs)
trainable_vars = model.get_trainable_vars()
# Now there should be only 6 trainable vars:
# 141 block4/unit_2/bottleneck_v1/conv1/weights:0
# 142 block4/unit_2/bottleneck_v1/conv1/BatchNorm/beta:0
# 143 block4/unit_2/bottleneck_v1/conv1/BatchNorm/gamma:0
# 144 block4/unit_2/bottleneck_v1/conv2/weights:0
# 145 block4/unit_2/bottleneck_v1/conv2/BatchNorm/beta:0
# 146 block4/unit_2/bottleneck_v1/conv2/BatchNorm/gamma:0
self.assertEqual(len(trainable_vars), 6)
#
# Check that we return no vars if fine_tune_from is after the chosen
# endpoint (there is nothing to fine-tune!) and tail is frozen.
#
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv1',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv2',
'freeze_tail': True,
'use_tail': True,
}))
model(inputs)
self.assertEqual(len(model.get_trainable_vars()), 0)
def testTrainableVariables(self):
inputs = tf.placeholder(tf.float32, [1, 224, 224, 3])
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_3/bottleneck_v1/conv2',
'freeze_tail': False,
'use_tail': True,
})
)
model(inputs)
# Variables in ResNet-50:
# (the order of beta and gamma depends on the TensorFlow's version)
# 0 conv1/weights:0
# 1 conv1/BatchNorm/(beta|gamma):0
# 2 conv1/BatchNorm/(beta|gamma):0
# 3 block1/unit_1/bottleneck_v1/shortcut/weights:0
# (...)
# 153 block4/unit_3/bottleneck_v1/conv2/weights:0
# 154 block4/unit_3/bottleneck_v1/conv2/BatchNorm/(beta|gamma):0
# 155 block4/unit_3/bottleneck_v1/conv2/BatchNorm/(beta|gamma):0
# --- endpoint ---
# 156 block4/unit_3/bottleneck_v1/conv3/weights:0
# 157 block4/unit_3/bottleneck_v1/conv3/BatchNorm/(beta|gamma):0
# 158 block4/unit_3/bottleneck_v1/conv3/BatchNorm/(beta|gamma):0
# 159 logits/weights:0
# 160 logits/biases:0
trainable_vars = model.get_trainable_vars()
self.assertEqual(len(trainable_vars), 156)
self.assertEqual(
trainable_vars[-3].name,
'truncated_base_network/resnet_v1_50/' +
'block4/unit_3/bottleneck_v1/conv2/weights:0'
)
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv2',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv1',
'freeze_tail': False,
'use_tail': True,
})
)
model(inputs)
trainable_vars = model.get_trainable_vars()
# Now there should be only 6 trainable vars:
# 141 block4/unit_2/bottleneck_v1/conv1/weights:0
# 142 block4/unit_2/bottleneck_v1/conv1/BatchNorm/beta:0
# 143 block4/unit_2/bottleneck_v1/conv1/BatchNorm/gamma:0
# 144 block4/unit_2/bottleneck_v1/conv2/weights:0
# 145 block4/unit_2/bottleneck_v1/conv2/BatchNorm/beta:0
# 146 block4/unit_2/bottleneck_v1/conv2/BatchNorm/gamma:0
self.assertEqual(len(trainable_vars), 6)
#
# Check that we return no vars if fine_tune_from is after the chosen
# endpoint (there is nothing to fine-tune!) and tail is frozen.
#
model = TruncatedBaseNetwork(
easydict.EasyDict(
{
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv1',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv2',
'freeze_tail': True,
'use_tail': True,
}
)
)
model(inputs)
self.assertEqual(len(model.get_trainable_vars()), 0)
def testTrainableVariables(self):
inputs = tf.placeholder(tf.float32, [1, 224, 224, 3])
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_3/bottleneck_v1/conv2'
})
)
model(inputs)
# Variables in ResNet-50:
# 0 conv1/weights:0
# 1 conv1/BatchNorm/beta:0
# 2 conv1/BatchNorm/gamma:0
# 3 block1/unit_1/bottleneck_v1/shortcut/weights:0
# (...)
# 153 block4/unit_3/bottleneck_v1/conv2/weights:0
# 154 block4/unit_3/bottleneck_v1/conv2/BatchNorm/beta:0
# 155 block4/unit_3/bottleneck_v1/conv2/BatchNorm/gamma:0
# --- endpoint ---
# 156 block4/unit_3/bottleneck_v1/conv3/weights:0
# 157 block4/unit_3/bottleneck_v1/conv3/BatchNorm/beta:0
# 158 block4/unit_3/bottleneck_v1/conv3/BatchNorm/gamma:0
# 159 logits/weights:0
# 160 logits/biases:0
trainable_vars = model.get_trainable_vars()
self.assertEquals(len(trainable_vars), 156)
self.assertEquals(
trainable_vars[-1].name,
'truncated_base_network/resnet_v1_50/' +
'block4/unit_3/bottleneck_v1/conv2/BatchNorm/gamma:0'
)
model = TruncatedBaseNetwork(
easydict.EasyDict({
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv2',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv1',
})
)
model(inputs)
trainable_vars = model.get_trainable_vars()
# Now there should be only 6 trainable vars:
# 141 block4/unit_2/bottleneck_v1/conv1/weights:0
# 142 block4/unit_2/bottleneck_v1/conv1/BatchNorm/beta:0
# 143 block4/unit_2/bottleneck_v1/conv1/BatchNorm/gamma:0
# 144 block4/unit_2/bottleneck_v1/conv2/weights:0
# 145 block4/unit_2/bottleneck_v1/conv2/BatchNorm/beta:0
# 146 block4/unit_2/bottleneck_v1/conv2/BatchNorm/gamma:0
self.assertEquals(len(trainable_vars), 6)
#
# Check that we raise proper exception if fine_tune_from is after
# the chosen endpoint (there is nothing to fine-tune!)
#
model = TruncatedBaseNetwork(
easydict.EasyDict(
{
'architecture': 'resnet_v1_50',
'endpoint': 'block4/unit_2/bottleneck_v1/conv1',
'fine_tune_from': 'block4/unit_2/bottleneck_v1/conv2',
}
)
)
model(inputs)
with self.assertRaises(ValueError):
model.get_trainable_vars()