Python keras.utils.conv_utils.conv_output_length() Examples
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code examples of keras.utils.conv_utils.conv_output_length().
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Example #1
| Source File: sn.py From Coloring-greyscale-images with MIT License | 7 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.filters,)
if self.data_format == 'channels_first':
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0], self.filters) + tuple(new_space)
Example #2
| Source File: depthwise_conv2d.py From keras-mobilenet with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], self.filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, self.filters)
Example #3
| Source File: mobilenet.py From keras-FP16-test with Apache License 2.0 | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #4
| Source File: mobilenet.py From deep-learning-models with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #5
| Source File: mobilenetv2.py From mobilenet_v2_keras with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #6
| Source File: mobilenets-checkpoint.py From CBAM-keras with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #7
| Source File: mobilenets.py From CBAM-keras with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #8
| Source File: ConvolutionalMoE.py From mixture-of-experts with GNU General Public License v3.0 | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.n_filters,)
if self.data_format == 'channels_first':
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0], self.n_filters) + tuple(new_space)
Example #9
| Source File: gc_mobilenets.py From keras-global-context-networks with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #10
| Source File: se_mobilenets.py From keras-squeeze-excite-network with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return input_shape[0], out_filters, rows, cols
elif self.data_format == 'channels_last':
return input_shape[0], rows, cols, out_filters
Example #11
| Source File: utils.py From face_landmark_dnn with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding, self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding, self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #12
| Source File: train_mobilenets.py From face_landmark_dnn with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding, self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding, self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #13
| Source File: se_mobilenets.py From TF.Keras-Commonly-used-models with Apache License 2.0 | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #14
| Source File: mobile_net_fixed.py From kaggle-carvana-2017 with MIT License | 6 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
rows = input_shape[2]
cols = input_shape[3]
out_filters = input_shape[1] * self.depth_multiplier
elif self.data_format == 'channels_last':
rows = input_shape[1]
cols = input_shape[2]
out_filters = input_shape[3] * self.depth_multiplier
rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
self.padding,
self.strides[0])
cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
self.padding,
self.strides[1])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], rows, cols, out_filters)
Example #15
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_input_length():
assert conv_utils.conv_input_length(None, 7, 'same', 1) is None
assert conv_utils.conv_input_length(112, 7, 'same', 1) == 112
assert conv_utils.conv_input_length(112, 7, 'same', 2) == 223
assert conv_utils.conv_input_length(28, 5, 'valid', 1) == 32
assert conv_utils.conv_input_length(14, 5, 'valid', 2) == 31
assert conv_utils.conv_input_length(36, 5, 'full', 1) == 32
assert conv_utils.conv_input_length(18, 5, 'full', 2) == 31
with pytest.raises(AssertionError):
conv_utils.conv_output_length(18, 5, 'diagonal', 2)
Example #16
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_output_length():
assert conv_utils.conv_output_length(None, 7, 'same', 1) is None
assert conv_utils.conv_output_length(224, 7, 'same', 1) == 224
assert conv_utils.conv_output_length(224, 7, 'same', 2) == 112
assert conv_utils.conv_output_length(32, 5, 'valid', 1) == 28
assert conv_utils.conv_output_length(32, 5, 'valid', 2) == 14
assert conv_utils.conv_output_length(32, 5, 'causal', 1) == 32
assert conv_utils.conv_output_length(32, 5, 'causal', 2) == 16
assert conv_utils.conv_output_length(32, 5, 'full', 1) == 36
assert conv_utils.conv_output_length(32, 5, 'full', 2) == 18
with pytest.raises(AssertionError):
conv_utils.conv_output_length(32, 5, 'diagonal', 2)
Example #17
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_input_length():
assert conv_utils.conv_input_length(None, 7, 'same', 1) is None
assert conv_utils.conv_input_length(112, 7, 'same', 1) == 112
assert conv_utils.conv_input_length(112, 7, 'same', 2) == 223
assert conv_utils.conv_input_length(28, 5, 'valid', 1) == 32
assert conv_utils.conv_input_length(14, 5, 'valid', 2) == 31
assert conv_utils.conv_input_length(36, 5, 'full', 1) == 32
assert conv_utils.conv_input_length(18, 5, 'full', 2) == 31
with pytest.raises(AssertionError):
conv_utils.conv_output_length(18, 5, 'diagonal', 2)
Example #18
| Source File: DepthwiseConv3D.py From keras-DepthwiseConv3D with MIT License | 5 votes |
|
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
depth = input_shape[2]
rows = input_shape[3]
cols = input_shape[4]
out_filters = self.groups * self.depth_multiplier
elif self.data_format == 'channels_last':
depth = input_shape[1]
rows = input_shape[2]
cols = input_shape[3]
out_filters = self.groups * self.depth_multiplier
depth = conv_utils.conv_output_length(depth, self.kernel_size[0],
self.padding,
self.strides[0])
rows = conv_utils.conv_output_length(rows, self.kernel_size[1],
self.padding,
self.strides[1])
cols = conv_utils.conv_output_length(cols, self.kernel_size[2],
self.padding,
self.strides[2])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, depth, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], depth, rows, cols, out_filters)
Example #19
| Source File: qrnn.py From nn_playground with MIT License | 5 votes |
|
def compute_output_shape(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
length = input_shape[1]
if length:
length = conv_output_length(length + self.window_size - 1,
self.window_size, 'valid',
self.strides[0])
if self.return_sequences:
return (input_shape[0], length, self.units)
else:
return (input_shape[0], self.units)
Example #20
| Source File: gcnn.py From nn_playground with MIT License | 5 votes |
|
def compute_output_shape(self, input_shape):
length = input_shape[1]
if length:
length = conv_output_length(length + self.window_size - 1,
self.window_size, 'valid',
self.strides[0])
return (input_shape[0], length, self.output_dim)
Example #21
| Source File: capsule_layers.py From SegCaps with Apache License 2.0 | 5 votes |
|
def compute_output_shape(self, input_shape):
space = input_shape[1:-2]
new_space = []
for i in range(len(space)):
new_dim = conv_output_length(
space[i],
self.kernel_size,
padding=self.padding,
stride=self.strides,
dilation=1)
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.num_capsule, self.num_atoms)
Example #22
| Source File: capslayers.py From deepcaps with MIT License | 5 votes |
|
def build(self, input_shape):
self.h_i, self.w_i, self.ch_i, self.n_i = input_shape[1:5]
self.h_j, self.w_j = [conv_utils.conv_output_length(input_shape[i + 1],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i]) for i in (0, 1)]
self.ah_j, self.aw_j = [conv_utils.conv_output_length(input_shape[i + 1],
self.kernel_size[i],
padding=self.padding,
stride=1,
dilation=self.dilation_rate[i]) for i in (0, 1)]
self.w_shape = self.kernel_size + (self.ch_i, self.n_i,
self.ch_j, self.n_j)
self.w = self.add_weight(shape=self.w_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.built = True
Example #23
| Source File: capslayers.py From deepcaps with MIT License | 5 votes |
|
def compute_output_shape(self, input_shape):
space = input_shape[1:-2]
new_space = []
for i in range(len(space)):
new_dim = conv_output_length(space[i], self.kernel_size, padding=self.padding, stride=self.strides, dilation=1)
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.num_capsule, self.num_atoms)
Example #24
| Source File: gram.py From subjective-functions with MIT License | 5 votes |
|
def reduce_layer(a=0.4, padding_mode='valid'):
# A 5-tap Gaussian pyramid generating kernel from Burt & Adelson 1983.
kernel_1d = [0.25 - a/2, 0.25, a, 0.25, 0.25 - a/2]
#kernel_2d = np.outer(kernel_1d, kernel_1d)
# This doesn't seem very computationally bright; but there you have it.
#kernel_4d = np.zeros((5, 5, 3, 3), 'float32')
#kernel_4d[:,:,0,0] = kernel_2d
#kernel_4d[:,:,1,1] = kernel_2d
#kernel_4d[:,:,2,2] = kernel_2d
kernel_3d = np.zeros((5, 1, 3, 3), 'float32')
kernel_3d[:, 0, 0, 0] = kernel_1d
kernel_3d[:, 0, 1, 1] = kernel_1d
kernel_3d[:, 0, 2, 2] = kernel_1d
def fn(x):
return K.conv2d(K.conv2d(x, kernel_3d, strides=(2,1)),
K.permute_dimensions(kernel_3d, (1, 0, 2, 3)),
strides = (1, 2))
def shape(input_shape):
assert len(input_shape) == 4
assert K.image_data_format() == 'channels_last'
space = input_shape[1:-1]
new_space = []
for i, dim in enumerate(space):
new_dim = conv_utils.conv_output_length(
dim,
5,
padding=padding_mode,
stride=2)
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (input_shape[3],)
return Lambda(fn, shape)
Example #25
| Source File: custom_objects.py From keras_mixnets with MIT License | 5 votes |
|
def compute_output_shape(self, input_shape):
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
filter_size=1,
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.filters,)
Example #26
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_output_length():
assert conv_utils.conv_output_length(None, 7, 'same', 1) is None
assert conv_utils.conv_output_length(224, 7, 'same', 1) == 224
assert conv_utils.conv_output_length(224, 7, 'same', 2) == 112
assert conv_utils.conv_output_length(32, 5, 'valid', 1) == 28
assert conv_utils.conv_output_length(32, 5, 'valid', 2) == 14
assert conv_utils.conv_output_length(32, 5, 'causal', 1) == 32
assert conv_utils.conv_output_length(32, 5, 'causal', 2) == 16
assert conv_utils.conv_output_length(32, 5, 'full', 1) == 36
assert conv_utils.conv_output_length(32, 5, 'full', 2) == 18
with pytest.raises(AssertionError):
conv_utils.conv_output_length(32, 5, 'diagonal', 2)
Example #27
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_output_length():
assert conv_utils.conv_output_length(None, 7, 'same', 1) is None
assert conv_utils.conv_output_length(224, 7, 'same', 1) == 224
assert conv_utils.conv_output_length(224, 7, 'same', 2) == 112
assert conv_utils.conv_output_length(32, 5, 'valid', 1) == 28
assert conv_utils.conv_output_length(32, 5, 'valid', 2) == 14
assert conv_utils.conv_output_length(32, 5, 'causal', 1) == 32
assert conv_utils.conv_output_length(32, 5, 'causal', 2) == 16
assert conv_utils.conv_output_length(32, 5, 'full', 1) == 36
assert conv_utils.conv_output_length(32, 5, 'full', 2) == 18
with pytest.raises(AssertionError):
conv_utils.conv_output_length(32, 5, 'diagonal', 2)
Example #28
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_input_length():
assert conv_utils.conv_input_length(None, 7, 'same', 1) is None
assert conv_utils.conv_input_length(112, 7, 'same', 1) == 112
assert conv_utils.conv_input_length(112, 7, 'same', 2) == 223
assert conv_utils.conv_input_length(28, 5, 'valid', 1) == 32
assert conv_utils.conv_input_length(14, 5, 'valid', 2) == 31
assert conv_utils.conv_input_length(36, 5, 'full', 1) == 32
assert conv_utils.conv_input_length(18, 5, 'full', 2) == 31
with pytest.raises(AssertionError):
conv_utils.conv_output_length(18, 5, 'diagonal', 2)
Example #29
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_output_length():
assert conv_utils.conv_output_length(None, 7, 'same', 1) is None
assert conv_utils.conv_output_length(224, 7, 'same', 1) == 224
assert conv_utils.conv_output_length(224, 7, 'same', 2) == 112
assert conv_utils.conv_output_length(32, 5, 'valid', 1) == 28
assert conv_utils.conv_output_length(32, 5, 'valid', 2) == 14
assert conv_utils.conv_output_length(32, 5, 'causal', 1) == 32
assert conv_utils.conv_output_length(32, 5, 'causal', 2) == 16
assert conv_utils.conv_output_length(32, 5, 'full', 1) == 36
assert conv_utils.conv_output_length(32, 5, 'full', 2) == 18
with pytest.raises(AssertionError):
conv_utils.conv_output_length(32, 5, 'diagonal', 2)
Example #30
| Source File: conv_utils_test.py From DeepLearning_Wavelet-LSTM with MIT License | 5 votes |
|
def test_conv_input_length():
assert conv_utils.conv_input_length(None, 7, 'same', 1) is None
assert conv_utils.conv_input_length(112, 7, 'same', 1) == 112
assert conv_utils.conv_input_length(112, 7, 'same', 2) == 223
assert conv_utils.conv_input_length(28, 5, 'valid', 1) == 32
assert conv_utils.conv_input_length(14, 5, 'valid', 2) == 31
assert conv_utils.conv_input_length(36, 5, 'full', 1) == 32
assert conv_utils.conv_input_length(18, 5, 'full', 2) == 31
with pytest.raises(AssertionError):
conv_utils.conv_output_length(18, 5, 'diagonal', 2)
