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keras/keras_core/layers/convolutional/conv_test.py
Chen Qian 43e33ab9ab Add Conv layers (#89) 
* initials

* more

* something

* add docstrings

* fix some docstrings

* fix comments
2023-05-05 22:13:13 -07:00

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import numpy as np
import tensorflow as tf
from absl.testing import parameterized
from keras_core import layers
from keras_core import testing
class ConvBasicTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
"input_shape": (3, 5, 4),
"output_shape": (3, 4, 5),
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2,),
"groups": 2,
"input_shape": (3, 4, 4),
"output_shape": (3, 4, 6),
},
{
"filters": 6,
"kernel_size": 2,
"strides": (2,),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 2,
"input_shape": (3, 5, 4),
"output_shape": (3, 2, 6),
},
)
def test_conv1d_basic(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
input_shape,
output_shape,
):
self.run_layer_test(
layers.Conv1D,
init_kwargs={
"filters": filters,
"kernel_size": kernel_size,
"strides": strides,
"padding": padding,
"data_format": data_format,
"dilation_rate": dilation_rate,
"groups": groups,
},
input_shape=input_shape,
expected_output_shape=output_shape,
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_losses=0,
supports_masking=False,
)
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
"input_shape": (3, 5, 5, 4),
"output_shape": (3, 4, 4, 5),
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2, 2),
"groups": 2,
"input_shape": (3, 4, 4, 4),
"output_shape": (3, 4, 4, 6),
},
{
"filters": 6,
"kernel_size": (2, 2),
"strides": (2, 1),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": (1, 1),
"groups": 2,
"input_shape": (3, 5, 5, 4),
"output_shape": (3, 2, 4, 6),
},
)
def test_conv2d_basic(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
input_shape,
output_shape,
):
self.run_layer_test(
layers.Conv2D,
init_kwargs={
"filters": filters,
"kernel_size": kernel_size,
"strides": strides,
"padding": padding,
"data_format": data_format,
"dilation_rate": dilation_rate,
"groups": groups,
},
input_shape=input_shape,
expected_output_shape=output_shape,
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_losses=0,
supports_masking=False,
)
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
"input_shape": (3, 5, 5, 5, 4),
"output_shape": (3, 4, 4, 4, 5),
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2, 2, 2),
"groups": 2,
"input_shape": (3, 4, 4, 4, 4),
"output_shape": (3, 4, 4, 4, 6),
},
{
"filters": 6,
"kernel_size": (2, 2, 3),
"strides": (2, 1, 2),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": (1, 1, 1),
"groups": 2,
"input_shape": (3, 5, 5, 5, 4),
"output_shape": (3, 2, 4, 2, 6),
},
)
def test_conv3d_basic(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
input_shape,
output_shape,
):
self.run_layer_test(
layers.Conv3D,
init_kwargs={
"filters": filters,
"kernel_size": kernel_size,
"strides": strides,
"padding": padding,
"data_format": data_format,
"dilation_rate": dilation_rate,
"groups": groups,
},
input_shape=input_shape,
expected_output_shape=output_shape,
expected_num_trainable_weights=2,
expected_num_non_trainable_weights=0,
expected_num_losses=0,
supports_masking=False,
)
def test_bad_init_args(self):
# `filters` is not positive.
with self.assertRaises(ValueError):
layers.Conv1D(filters=0, kernel_size=1)
# `kernel_size` has 0.
with self.assertRaises(ValueError):
layers.Conv2D(filters=2, kernel_size=(1, 0))
# `strides` has 0.
with self.assertRaises(ValueError):
layers.Conv2D(filters=2, kernel_size=(2, 2), strides=(1, 0))
# `dilation_rate > 1` while `strides > 1`.
with self.assertRaises(ValueError):
layers.Conv2D(
filters=2, kernel_size=(2, 2), strides=2, dilation_rate=(2, 1)
)
# `filters` cannot be divided by `groups`.
with self.assertRaises(ValueError):
layers.Conv2D(filters=5, kernel_size=(2, 2), groups=2)
class ConvCorrectnessTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2,),
"groups": 2,
},
{
"filters": 6,
"kernel_size": (2,),
"strides": (2,),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 2,
},
)
def test_conv1d(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
):
layer = layers.Conv1D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
tf_keras_layer = tf.keras.layers.Conv1D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
inputs = np.random.normal(size=[2, 8, 4])
layer.build(input_shape=inputs.shape)
tf_keras_layer.build(input_shape=inputs.shape)
kernel_shape = layer.kernel.shape
kernel_weights = np.random.normal(size=kernel_shape)
bias_weights = np.random.normal(size=(filters,))
layer.kernel.assign(kernel_weights)
tf_keras_layer.kernel.assign(kernel_weights)
layer.bias.assign(bias_weights)
tf_keras_layer.bias.assign(bias_weights)
outputs = layer(inputs)
expected = tf_keras_layer(inputs)
self.assertAllClose(outputs, expected)
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2, 2),
"groups": 2,
},
{
"filters": 6,
"kernel_size": (2, 2),
"strides": (2, 1),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": (1, 1),
"groups": 2,
},
)
def test_conv2d(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
):
layer = layers.Conv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
tf_keras_layer = tf.keras.layers.Conv2D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
inputs = np.random.normal(size=[2, 8, 8, 4])
layer.build(input_shape=inputs.shape)
tf_keras_layer.build(input_shape=inputs.shape)
kernel_shape = layer.kernel.shape
kernel_weights = np.random.normal(size=kernel_shape)
bias_weights = np.random.normal(size=(filters,))
layer.kernel.assign(kernel_weights)
tf_keras_layer.kernel.assign(kernel_weights)
layer.bias.assign(bias_weights)
tf_keras_layer.bias.assign(bias_weights)
outputs = layer(inputs)
expected = tf_keras_layer(inputs)
self.assertAllClose(outputs, expected)
@parameterized.parameters(
{
"filters": 5,
"kernel_size": 2,
"strides": 1,
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": 1,
"groups": 1,
},
{
"filters": 6,
"kernel_size": 2,
"strides": 1,
"padding": "same",
"data_format": "channels_last",
"dilation_rate": (2, 2, 2),
"groups": 2,
},
{
"filters": 6,
"kernel_size": (2, 2, 3),
"strides": (2, 1, 2),
"padding": "valid",
"data_format": "channels_last",
"dilation_rate": (1, 1, 1),
"groups": 2,
},
)
def test_conv3d(
self,
filters,
kernel_size,
strides,
padding,
data_format,
dilation_rate,
groups,
):
layer = layers.Conv3D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
tf_keras_layer = tf.keras.layers.Conv3D(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
groups=groups,
)
inputs = np.random.normal(size=[2, 8, 8, 8, 4])
layer.build(input_shape=inputs.shape)
tf_keras_layer.build(input_shape=inputs.shape)
kernel_shape = layer.kernel.shape
kernel_weights = np.random.normal(size=kernel_shape)
bias_weights = np.random.normal(size=(filters,))
layer.kernel.assign(kernel_weights)
tf_keras_layer.kernel.assign(kernel_weights)
layer.bias.assign(bias_weights)
tf_keras_layer.bias.assign(bias_weights)
outputs = layer(inputs)
expected = tf_keras_layer(inputs)
self.assertAllClose(outputs, expected)
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