def rebuild_image():
h_fc1 = tf.nn.relu(tf.matmul(y_ + b_fc2, W_fc2))
h_pool2_flat = tf.matmul(h_fc1 + b_fc1, W_fc1)
h_pool2 = tf.reshape(h_pool2_flat, [-1, 7, 7, conv2_size]) # I think that's right...
h_conv2 = tf.image.resize_images(h_pool2,14,14, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
h_pool1 = nn_ops.conv2d_transpose(h_conv2 + b_conv2,W_conv2,
[class_size,14,14,conv1_size],[1,1,1,1])
h_conv1 = tf.image.resize_images(h_pool1 ,28,28,method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
x_image = nn_ops.conv2d_transpose(h_conv1 + b_conv1, W_conv1, [class_size,28,28,1], [1,1,1,1])
x_image = tf.nn.relu(x_image)
return x_image
def testConv2DTransposeValid(self):
with self.cached_session():
for dtype in (dtypes.float32, dtypes.int32):
strides = [1, 2, 2, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 13, 9, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(1,
shape=x_shape,
name="x",
dtype=dtype)
f = constant_op.constant(1,
shape=f_shape,
name="filter",
dtype=dtype)
output = nn_ops.conv2d_transpose(x,
f,
y_shape,
strides=strides,
padding="VALID")
value = self.evaluate(output)
cache_values = np.zeros(y_shape, dtype=np.float32)
# The amount of padding added
pad = 1
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(pad, y_shape[2] - pad):
for h in xrange(pad, y_shape[1] - pad):
target = 3
# We add a case for locations divisible by the stride.
h_in = h % strides[
1] == 0 and h > pad and h < y_shape[
1] - 1 - pad
w_in = w % strides[
2] == 0 and w > pad and w < y_shape[
2] - 1 - pad
if h_in and w_in:
target += 9
elif h_in or w_in:
target += 3
cache_values[n, h, w, k] = target
# copy values in the border
cache_values[n, :, 0, k] = cache_values[n, :, 1, k]
cache_values[n, :, -1, k] = cache_values[n, :, -2, k]
cache_values[n, 0, :, k] = cache_values[n, 1, :, k]
cache_values[n, -1, :, k] = cache_values[n, -2, :, k]
if dtype.is_integer:
self.assertAllEqual(cache_values, value)
else:
self.assertAllClose(cache_values, value)
def _test_transpose_conv(tensor_in_sizes, filter_in_sizes, output_shape, strides, padding):
""" One iteration of transpose convolution with given shapes and attributes """
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
strides = [1] + strides + [1]
# in_filter layout is HWOI
out = nn_ops.conv2d_transpose(in_data,
in_filter,
output_shape=output_shape,
strides=strides,
padding=padding)
data_array = np.reshape(data_array, tensor_in_sizes).astype('float32')
compare_tflite_with_tvm(data_array, 'Placeholder:0', [in_data], [out])
def testConv2DTransposeSame(self):
with self.test_session():
strides = [1, 2, 2, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 12, 8, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
value = output.eval()
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[1] == 0 and h > 0 and h < y_shape[1] - 1
w_in = w % strides[2] == 0 and w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
self.assertAllClose(target, value[n, h, w, k])
def GetParams(self):
"""Testing conversion of conv2d_transpose (AKA Conv2DBackpropInput)"""
np.random.seed(1234)
dtype = dtypes.float32
input_name = "input"
n, c, h, w = 13, 3, 7, 11
num_filters = 8
input_dims = [n, c, h, w]
output_name = "output"
g = ops.Graph()
with g.as_default():
inp = array_ops.placeholder(
dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
with g.device("/GPU:0"):
weights_shape = [2, 2, num_filters, c]
weights = constant_op.constant(
np.random.randn(*weights_shape), dtype=dtype)
output_shape = constant_op.constant([n, num_filters, h * 2, w * 2],
dtype=dtypes.int32)
output = nn_ops.conv2d_transpose(
inp,
weights,
output_shape,
strides=[1, 1, 2, 2],
padding="SAME",
data_format="NCHW")
output = array_ops.identity(output, name=output_name)
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[[input_dims]],
output_names=[output_name],
expected_output_dims=[[[n, num_filters, h * 2, w * 2]]])
def rebuild_image():
h_fc1 = tf.nn.relu(tf.matmul(y_ + b_fc2, W_fc2))
h_pool2_flat = tf.matmul(h_fc1 + b_fc1, W_fc1)
h_pool2 = tf.reshape(h_pool2_flat,
[-1, 7, 7, conv2_size]) # I think that's right...
h_conv2 = tf.image.resize_images(
h_pool2, 14, 14, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
h_pool1 = nn_ops.conv2d_transpose(h_conv2 + b_conv2, W_conv2,
[class_size, 14, 14, conv1_size],
[1, 1, 1, 1])
h_conv1 = tf.image.resize_images(
h_pool1, 28, 28, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
x_image = nn_ops.conv2d_transpose(h_conv1 + b_conv1, W_conv1,
[class_size, 28, 28, 1], [1, 1, 1, 1])
x_image = tf.nn.relu(x_image)
return x_image
def testConv2DTransposeSameNCHW(self):
# `NCHW` data format is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.session(use_gpu=True):
strides = [1, 1, 2, 2]
# Input, output: [batch, depth, height, width]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 12, 8]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME", data_format="NCHW")
value = self.evaluate(output)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[3]):
for h in xrange(y_shape[2]):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[2] == 0 and h > 0 and h < y_shape[2] - 1
w_in = w % strides[3] == 0 and w > 0 and w < y_shape[3] - 1
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
self.assertAllClose(target, value[n, k, h, w])
def testAtrousConv2DTransposeForward(self):
with self.session(use_gpu=True):
# Input: [batch, height, width, input_depth]
height = 9
for width in [9, 10]: # Test both odd and even width.
x_shape = [2, height, width, 2]
x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
for kernel_height in range(1, 4):
for kernel_width in range(1, 4):
f_shape = [kernel_height, kernel_width, 2, 2]
f = np.arange(np.prod(f_shape), dtype=np.float32).reshape(f_shape)
for rate in range(1, 4):
f_up = _upsample_filters(f, rate)
kernel_height_up = (kernel_height + (kernel_height - 1) *
(rate - 1))
kernel_width_up = kernel_width + (kernel_width - 1) * (rate - 1)
for padding in ["SAME", "VALID"]:
if padding == "SAME":
y_shape = [2, height, width, 2]
else:
y_shape = [
2, height + kernel_height_up - 1,
width + kernel_width_up - 1, 2
]
y1 = nn_ops.atrous_conv2d_transpose(x, f, y_shape, rate,
padding)
y2 = nn_ops.conv2d_transpose(
x, f_up, y_shape, strides=[1, 1, 1, 1], padding=padding)
self.assertAllClose(
y1.eval(), self.evaluate(y2), rtol=1e-3, atol=1e-3)
def testConv2DTransposeSameNCHW(self):
# `NCHW` data fomat is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 2, 2]
# Input, output: [batch, depth, height, width]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 12, 8]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME", data_format="NCHW")
value = output.eval()
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[3]):
for h in xrange(y_shape[2]):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[2] == 0 and h > 0 and h < y_shape[2] - 1
w_in = w % strides[3] == 0 and w > 0 and w < y_shape[3] - 1
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
self.assertAllClose(target, value[n, k, h, w])
def testConv2DTransposeSame(self):
with self.cached_session():
strides = [1, 2, 2, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 12, 8, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
value = self.evaluate(output)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[1] == 0 and h > 0 and h < y_shape[1] - 1
w_in = w % strides[2] == 0 and w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
self.assertAllClose(target, value[n, h, w, k])
def testConv2DTransposeSingleStrideNCHW(self):
# `NCHW` data format is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.session(use_gpu=True):
strides = [1, 1, 1, 1]
# Input, output: [batch, depth, height, width, depth]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 6, 4]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME", data_format="NCHW")
value = self.evaluate(output)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[3]):
for h in xrange(y_shape[2]):
target = 4 * 3.0
h_in = h > 0 and h < y_shape[2] - 1
w_in = w > 0 and w < y_shape[3] - 1
if h_in and w_in:
target += 5 * 3.0
elif h_in or w_in:
target += 2 * 3.0
self.assertAllClose(target, value[n, k, h, w])
def testConv2DTransposeValidNCHW(self):
# `NCHW` data fomat is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 2, 2]
# Input, output: [batch, depth, height, width]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 13, 9]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(1.0,
shape=x_shape,
name="x",
dtype=dtypes.float32)
f = constant_op.constant(1.0,
shape=f_shape,
name="filter",
dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(x,
f,
y_shape,
strides=strides,
padding="VALID",
data_format="NCHW")
value = output.eval()
cache_values = np.zeros(y_shape, dtype=np.float32)
# The amount of padding added
pad = 1
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(pad, y_shape[3] - pad):
for h in xrange(pad, y_shape[2] - pad):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[
2] == 0 and h > pad and h < y_shape[
2] - 1 - pad
w_in = w % strides[
3] == 0 and w > pad and w < y_shape[
3] - 1 - pad
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
cache_values[n, k, h, w] = target
# copy values in the border
cache_values[n, k, :, 0] = cache_values[n, k, :, 1]
cache_values[n, k, :, -1] = cache_values[n, k, :, -2]
cache_values[n, k, 0, :] = cache_values[n, k, 1, :]
cache_values[n, k, -1, :] = cache_values[n, k, -2, :]
self.assertAllClose(cache_values, value)
def testConv2DTransposeShapeInference(self):
# Test case for 8972
initializer = random_ops.truncated_normal(
[3, 3, 5, 1], mean=0.0, stddev=0.01, dtype=dtypes.float32)
x = variables.Variable(random_ops.random_normal([3, 10, 5, 1]))
f = variable_scope.get_variable("f", initializer=initializer)
f_shape = array_ops.stack([array_ops.shape(x)[0], 10, 5, 5])
output = nn_ops.conv2d_transpose(
x, f, f_shape, strides=[1, 1, 1, 1], padding="SAME")
self.assertEqual(output.get_shape().as_list(), [None, 10, 5, 5])
def testConv2DTransposeShapeInference(self):
# Test case for 8972
initializer = random_ops.truncated_normal(
[3, 3, 5, 1], mean=0.0, stddev=0.01, dtype=dtypes.float32)
x = variables.Variable(random_ops.random_normal([3, 10, 5, 1]))
f = variable_scope.get_variable("f", initializer=initializer)
f_shape = array_ops.stack([array_ops.shape(x)[0], 10, 5, 5])
output = nn_ops.conv2d_transpose(
x, f, f_shape, strides=[1, 1, 1, 1], padding="SAME")
self.assertEqual(output.get_shape().as_list(), [3, 10, 5, 5])
def testConv2DTransposeSingleStride(self):
with self.cached_session():
for dtype in (dtypes.float32, dtypes.int32):
strides = [1, 1, 1, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 6, 4, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(1,
shape=x_shape,
name="x",
dtype=dtype)
f = constant_op.constant(1,
shape=f_shape,
name="filter",
dtype=dtype)
output = nn_ops.conv2d_transpose(x,
f,
y_shape,
strides=strides,
padding="SAME")
value = self.evaluate(output)
# We count the number of cells being added at the locations in the
# output.
# At the center, #cells=kernel_height * kernel_width
# At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2)
# At the borders, #cells=ceil(kernel_height/2)*kernel_width or
# kernel_height * ceil(kernel_width/2)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 4 * 3
h_in = h > 0 and h < y_shape[1] - 1
w_in = w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 5 * 3
elif h_in or w_in:
target += 2 * 3
if dtype.is_integer:
self.assertAllEqual(
target, value[n, h, w, k])
else:
self.assertAllClose(
target, value[n, h, w, k])
def testConv2DTransposeValid(self):
with self.test_session():
strides = [1, 2, 2, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 13, 9, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="VALID")
value = output.eval()
cache_values = np.zeros(y_shape, dtype=np.float32)
# The amount of padding added
pad = 1
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(pad, y_shape[2] - pad):
for h in xrange(pad, y_shape[1] - pad):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[1] == 0 and h > pad and h < y_shape[
1] - 1 - pad
w_in = w % strides[2] == 0 and w > pad and w < y_shape[
2] - 1 - pad
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
cache_values[n, h, w, k] = target
# copy values in the border
cache_values[n, :, 0, k] = cache_values[n, :, 1, k]
cache_values[n, :, -1, k] = cache_values[n, :, -2, k]
cache_values[n, 0, :, k] = cache_values[n, 1, :, k]
cache_values[n, -1, :, k] = cache_values[n, -2, :, k]
self.assertAllClose(cache_values, value)
def testConv2DTransposeValidNCHW(self):
# `NCHW` data fomat is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 2, 2]
# Input, output: [batch, depth, height, width]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 13, 9]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="VALID", data_format="NCHW")
value = output.eval()
cache_values = np.zeros(y_shape, dtype=np.float32)
# The amount of padding added
pad = 1
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(pad, y_shape[3] - pad):
for h in xrange(pad, y_shape[2] - pad):
target = 3.0
# We add a case for locations divisible by the stride.
h_in = h % strides[2] == 0 and h > pad and h < y_shape[
2] - 1 - pad
w_in = w % strides[3] == 0 and w > pad and w < y_shape[
3] - 1 - pad
if h_in and w_in:
target += 9.0
elif h_in or w_in:
target += 3.0
cache_values[n, k, h, w] = target
# copy values in the border
cache_values[n, k, :, 0] = cache_values[n, k, :, 1]
cache_values[n, k, :, -1] = cache_values[n, k, :, -2]
cache_values[n, k, 0, :] = cache_values[n, k, 1, :]
cache_values[n, k, -1, :] = cache_values[n, k, -2, :]
self.assertAllClose(cache_values, value)
def GraphFn(self, inp):
np.random.seed(1234)
dtype = inp.dtype
n, c, h, w = 13, 3, 7, 11
num_filters = 8
weights_shape = [2, 2, num_filters, c]
weights = constant_op.constant(np.random.randn(*weights_shape),
dtype=dtype)
output_shape = constant_op.constant([n, num_filters, h * 2 + 1, w * 2],
dtype=dtypes.int32)
output = nn_ops.conv2d_transpose(inp,
weights,
output_shape,
strides=[1, 1, 2, 2],
padding="VALID",
data_format="NCHW")
return array_ops.identity(output, name="output_0")
def conv2d_decoder(inputs,
encoder,
shapes,
strides,
scope=None,
activation=None,
weight_sharing=False,
reuse=False):
with variable_scope.variable_scope(scope or "decoder",
reuse=reuse) as varscope:
# Create a new scope in which the caching device is either
# determined by the parent scope, or is set to place the cached
if not context.executing_eagerly():
if varscope.caching_device is None:
varscope.set_caching_device(lambda op: op.device)
encoder.reverse()
shapes.reverse()
strides.reverse()
for idx, shape in enumerate(shapes):
encoder_W = encoder[idx]
dtype = encoder_W.dtype
W = encoder_W if weight_sharing else variable_scope.get_variable(
'w_{}'.format(idx),
encoder_W.get_shape().as_list(),
dtype,
initializer=init_ops.variance_scaling_initializer())
b = variable_scope.get_variable(
'b_decoder_{}'.format(idx), [W.get_shape().as_list()[2]],
dtype,
initializer=init_ops.zeros_initializer())
outputs = math_ops.add(
nn_ops.conv2d_transpose(
inputs,
W,
array_ops.stack([
array_ops.shape(inputs)[0], shape[1], shape[2],
shape[3]
]),
strides=[1, strides[idx], strides[idx], 1],
padding='SAME'), b)
if activation:
outputs = activation(outputs)
inputs = outputs
return inputs
def testGradient(self):
x_shape = [2, 6, 4, 3]
f_shape = [3, 3, 2, 3]
y_shape = [2, 12, 8, 2]
strides = [1, 2, 2, 1]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float64)
f_val = np.random.random_sample(f_shape).astype(np.float64)
with self.cached_session():
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
output, y_shape)
print("conv2d_transpose gradient err = %g " % err)
err_tolerance = 0.0005
self.assertLess(err, err_tolerance)
def testGradient(self):
x_shape = [2, 6, 4, 3]
f_shape = [3, 3, 2, 3]
y_shape = [2, 12, 8, 2]
strides = [1, 2, 2, 1]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float64)
f_val = np.random.random_sample(f_shape).astype(np.float64)
with self.test_session():
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
output, y_shape)
print("conv2d_transpose gradient err = %g " % err)
err_tolerance = 0.0005
self.assertLess(err, err_tolerance)
def testConv2DTransposeSingleStrideNCHW(self):
# `NCHW` data fomat is only supported for CUDA device.
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 1, 1]
# Input, output: [batch, depth, height, width, depth]
x_shape = [2, 3, 6, 4]
y_shape = [2, 2, 6, 4]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(1.0,
shape=x_shape,
name="x",
dtype=dtypes.float32)
f = constant_op.constant(1.0,
shape=f_shape,
name="filter",
dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(x,
f,
y_shape,
strides=strides,
padding="SAME",
data_format="NCHW")
value = output.eval()
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[3]):
for h in xrange(y_shape[2]):
target = 4 * 3.0
h_in = h > 0 and h < y_shape[2] - 1
w_in = w > 0 and w < y_shape[3] - 1
if h_in and w_in:
target += 5 * 3.0
elif h_in or w_in:
target += 2 * 3.0
self.assertAllClose(target, value[n, k, h, w])
def testConv2DTransposeSingleStride(self):
with self.test_session():
strides = [1, 1, 1, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 6, 4, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = constant_op.constant(
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
f = constant_op.constant(
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
output = nn_ops.conv2d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
value = output.eval()
# We count the number of cells being added at the locations in the output.
# At the center, #cells=kernel_height * kernel_width
# At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2)
# At the borders, #cells=ceil(kernel_height/2)*kernel_width or
# kernel_height * ceil(kernel_width/2)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 4 * 3.0
h_in = h > 0 and h < y_shape[1] - 1
w_in = w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 5 * 3.0
elif h_in or w_in:
target += 2 * 3.0
self.assertAllClose(target, value[n, h, w, k])
with open(dir + 'filter' + str(index) + '.png', "wb") as file:
t = tf.constant(t)
#t = tf.expand_dims(tf.constant(t), 0)
#t_n = tf.squeeze(nn_ops.conv2d_transpose(t, W_conv1, [1,5,5,1],[1,1,1,1]), [0])
t = tf.image.resize_images(t,
50,
50,
method=tf.image.ResizeMethod.BICUBIC)
t = tf.constant(ops.normalize(t.eval(), 0, 255))
file.write(tf.image.encode_png(t).eval())
index += 1
W_conv2_t = tf.transpose(W_conv2, perm=[3, 0, 1, 2])
index = 0
dir = "l2f/"
#for t in W_conv1_t.eval():
for t in W_conv2_t.eval():
with open(dir + 'filter' + str(index) + '.png', "wb") as file:
t = tf.expand_dims(tf.constant(t), 0)
t_n = tf.squeeze(
nn_ops.conv2d_transpose(t, W_conv1, [1, 5, 5, 1], [1, 1, 1, 1]),
[0])
t_n = tf.image.resize_images(t_n,
50,
50,
method=tf.image.ResizeMethod.BICUBIC)
t_n = tf.constant(ops.normalize(t_n.eval(), 0, 255))
file.write(tf.image.encode_png(t_n).eval())
index += 1
'''
h_pool1 = nn_ops.conv2d_transpose(h_conv2 + b_conv2,W_conv2,
[class_size,14,14,conv1_size],[1,1,1,1])
'''
index = 0
dir = "l1f/"
for t in W_conv1_t.eval():
with open(dir+'filter'+str(index)+'.png', "wb") as file:
t = tf.constant(t)
#t = tf.expand_dims(tf.constant(t), 0)
#t_n = tf.squeeze(nn_ops.conv2d_transpose(t, W_conv1, [1,5,5,1],[1,1,1,1]), [0])
t = tf.image.resize_images(t,50,50, method=tf.image.ResizeMethod.BICUBIC)
t = tf.constant(ops.normalize(t.eval(), 0, 255))
file.write(tf.image.encode_png(t).eval())
index += 1
W_conv2_t = tf.transpose(W_conv2, perm=[3,0,1,2])
index = 0
dir = "l2f/"
#for t in W_conv1_t.eval():
for t in W_conv2_t.eval():
with open(dir+'filter'+str(index)+'.png', "wb") as file:
t = tf.expand_dims(tf.constant(t), 0)
t_n = tf.squeeze(nn_ops.conv2d_transpose(t, W_conv1, [1,5,5,1],[1,1,1,1]), [0])
t_n = tf.image.resize_images(t_n,50,50, method=tf.image.ResizeMethod.BICUBIC)
t_n = tf.constant(ops.normalize(t_n.eval(), 0, 255))
file.write(tf.image.encode_png(t_n).eval())
index += 1