Python tensorflow.inv() Examples
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Example #1
Source File: videosr_ops_lite.py From SPMC_VideoSR with MIT License | 6 votes |
def ycbcr2rgb(inputs): with tf.name_scope('ycbcr2rgb'): if inputs.get_shape()[-1].value == 1: return inputs assert inputs.get_shape()[-1].value == 3, 'Error: rgb2ycbcr input should be RGB or grayscale!' ndims = len(inputs.get_shape()) # origT = np.array([[65.481, 128.553, 24.966], [-37.797 -74.203 112], [112 -93.786 -18.214]]) # T = tf.inv(origT) Tinv = [[0.00456621, 0., 0.00625893], [0.00456621, -0.00153632, -0.00318811], [0.00456621, 0.00791071, 0.]] origOffset = [16.0, 128.0, 128.0] if ndims == 4: origT = [tf.reshape(Tinv[i], [1, 1, 1, 3]) * 255.0 for i in xrange(3)] origOffset = tf.reshape(origOffset, [1, 1, 1, 3]) / 255.0 elif ndims == 5: origT = [tf.reshape(Tinv[i], [1, 1, 1, 1, 3]) * 255.0 for i in xrange(3)] origOffset = tf.reshape(origOffset, [1, 1, 1, 1, 3]) / 255.0 output = [] for i in xrange(3): output.append(tf.reduce_sum((inputs - origOffset) * origT[i], reduction_indices=-1, keep_dims=True)) return tf.concat(output, -1)
Example #2
Source File: videosr_ops.py From PFNL with MIT License | 6 votes |
def ycbcr2rgb(inputs): with tf.name_scope('ycbcr2rgb'): if inputs.get_shape()[-1].value == 1: return inputs assert inputs.get_shape()[-1].value == 3, 'Error: rgb2ycbcr input should be RGB or grayscale!' ndims = len(inputs.get_shape()) # origT = np.array([[65.481, 128.553, 24.966], [-37.797 -74.203 112], [112 -93.786 -18.214]]) # T = tf.inv(origT) Tinv = [[0.00456621, 0., 0.00625893], [0.00456621, -0.00153632, -0.00318811], [0.00456621, 0.00791071, 0.]] origOffset = [16.0, 128.0, 128.0] if ndims == 4: origT = [tf.reshape(Tinv[i], [1, 1, 1, 3]) * 255.0 for i in range(3)] origOffset = tf.reshape(origOffset, [1, 1, 1, 3]) / 255.0 elif ndims == 5: origT = [tf.reshape(Tinv[i], [1, 1, 1, 1, 3]) * 255.0 for i in range(3)] origOffset = tf.reshape(origOffset, [1, 1, 1, 1, 3]) / 255.0 output = [] for i in range(3): output.append(tf.reduce_sum((inputs - origOffset) * origT[i], reduction_indices=-1, keep_dims=True)) return tf.concat(output, -1)
Example #3
Source File: ops.py From tfdeploy with MIT License | 5 votes |
def test_Inv(self): if td._tf_version[:2] <= (0, 11): t = tf.inv(self.random(4, 3)) self.check(t)
Example #4
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testFloatBasic(self): x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32) y = (x + .5).astype(np.float32) # no zero z = (x + 15.5).astype(np.float32) # all positive k = np.arange(-0.90, 0.90, 0.25).astype(np.float32) # between -1 and 1 self._compareBoth(x, np.abs, tf.abs) self._compareBoth(x, np.abs, _ABS) self._compareBoth(x, np.negative, tf.neg) self._compareBoth(x, np.negative, _NEG) self._compareBoth(y, self._inv, tf.inv) self._compareBoth(x, np.square, tf.square) self._compareBoth(z, np.sqrt, tf.sqrt) self._compareBoth(z, self._rsqrt, tf.rsqrt) self._compareBoth(x, np.exp, tf.exp) self._compareBoth(z, np.log, tf.log) self._compareBoth(z, np.log1p, tf.log1p) self._compareBoth(x, np.tanh, tf.tanh) self._compareBoth(x, self._sigmoid, tf.sigmoid) self._compareBoth(y, np.sign, tf.sign) self._compareBoth(x, np.sin, tf.sin) self._compareBoth(x, np.cos, tf.cos) self._compareBoth(k, np.arcsin, tf.asin) self._compareBoth(k, np.arccos, tf.acos) self._compareBoth(x, np.arctan, tf.atan) self._compareBoth(x, np.tan, tf.tan) self._compareBoth( y, np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), tf.lgamma) self._compareBoth(x, np.vectorize(math.erf), tf.erf) self._compareBoth(x, np.vectorize(math.erfc), tf.erfc) self._compareBothSparse(x, np.abs, tf.abs) self._compareBothSparse(x, np.negative, tf.neg) self._compareBothSparse(x, np.square, tf.square) self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3) self._compareBothSparse(x, np.tanh, tf.tanh) self._compareBothSparse(y, np.sign, tf.sign) self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
Example #5
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testFloatEmpty(self): x = np.empty((2, 0, 5), dtype=np.float32) self._compareBoth(x, np.abs, tf.abs) self._compareBoth(x, np.abs, _ABS) self._compareBoth(x, np.negative, tf.neg) self._compareBoth(x, np.negative, _NEG) self._compareBoth(x, self._inv, tf.inv) self._compareBoth(x, np.square, tf.square) self._compareBoth(x, np.sqrt, tf.sqrt) self._compareBoth(x, self._rsqrt, tf.rsqrt) self._compareBoth(x, np.exp, tf.exp) self._compareBoth(x, np.log, tf.log) self._compareBoth(x, np.log1p, tf.log1p) self._compareBoth(x, np.tanh, tf.tanh) self._compareBoth(x, self._sigmoid, tf.sigmoid) self._compareBoth(x, np.sign, tf.sign) self._compareBoth(x, np.sin, tf.sin) self._compareBoth(x, np.cos, tf.cos) # Can't use vectorize below, so just use some arbitrary function self._compareBoth(x, np.sign, tf.lgamma) self._compareBoth(x, np.sign, tf.erf) self._compareBoth(x, np.sign, tf.erfc) self._compareBoth(x, np.tan, tf.tan) self._compareBoth(x, np.arcsin, tf.asin) self._compareBoth(x, np.arccos, tf.acos) self._compareBoth(x, np.arctan, tf.atan) self._compareBothSparse(x, np.abs, tf.abs) self._compareBothSparse(x, np.negative, tf.neg) self._compareBothSparse(x, np.square, tf.square) self._compareBothSparse(x, np.sqrt, tf.sqrt, tol=1e-3) self._compareBothSparse(x, np.tanh, tf.tanh) self._compareBothSparse(x, np.sign, tf.sign) self._compareBothSparse(x, np.sign, tf.erf)
Example #6
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testDoubleBasic(self): x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64) y = (x + .5).astype(np.float64) # no zero z = (x + 15.5).astype(np.float64) # all positive k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1 self._compareBoth(x, np.abs, tf.abs) self._compareBoth(x, np.abs, _ABS) self._compareBoth(x, np.negative, tf.neg) self._compareBoth(x, np.negative, _NEG) self._compareBoth(y, self._inv, tf.inv) self._compareBoth(x, np.square, tf.square) self._compareBoth(z, np.sqrt, tf.sqrt) self._compareBoth(z, self._rsqrt, tf.rsqrt) self._compareBoth(x, np.exp, tf.exp) self._compareBoth(z, np.log, tf.log) self._compareBoth(z, np.log1p, tf.log1p) self._compareBoth(x, np.tanh, tf.tanh) self._compareBoth(x, self._sigmoid, tf.sigmoid) self._compareBoth(y, np.sign, tf.sign) self._compareBoth(x, np.sin, tf.sin) self._compareBoth(x, np.cos, tf.cos) self._compareBoth( y, np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), tf.lgamma) self._compareBoth(x, np.vectorize(math.erf), tf.erf) self._compareBoth(x, np.vectorize(math.erfc), tf.erfc) self._compareBoth(x, np.arctan, tf.atan) self._compareBoth(k, np.arcsin, tf.asin) self._compareBoth(k, np.arccos, tf.acos) self._compareBoth(k, np.tan, tf.tan) self._compareBothSparse(x, np.abs, tf.abs) self._compareBothSparse(x, np.negative, tf.neg) self._compareBothSparse(x, np.square, tf.square) self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3) self._compareBothSparse(x, np.tanh, tf.tanh) self._compareBothSparse(y, np.sign, tf.sign) self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
Example #7
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testHalfBasic(self): x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float16) y = (x + .5).astype(np.float16) # no zero z = (x + 15.5).astype(np.float16) # all positive self._compareBoth(x, np.abs, tf.abs) self._compareBoth(x, np.abs, _ABS) self._compareBoth(x, np.negative, tf.neg) self._compareBoth(x, np.negative, _NEG) self._compareBoth(y, self._inv, tf.inv) self._compareBoth(x, np.square, tf.square) self._compareBoth(z, np.sqrt, tf.sqrt) self._compareBoth(z, self._rsqrt, tf.rsqrt) self._compareBoth(x, np.exp, tf.exp) self._compareBoth(z, np.log, tf.log) self._compareBoth(z, np.log1p, tf.log1p) self._compareBoth(x, np.tanh, tf.tanh) self._compareBoth(x, self._sigmoid, tf.sigmoid) self._compareBoth(y, np.sign, tf.sign) self._compareBoth(x, np.sin, tf.sin) self._compareBoth(x, np.cos, tf.cos) self._compareBoth( y, np.vectorize(self._replace_domain_error_with_inf(math.lgamma)), tf.lgamma) self._compareBoth(x, np.vectorize(math.erf), tf.erf) self._compareBoth(x, np.vectorize(math.erfc), tf.erfc) self._compareBothSparse(x, np.abs, tf.abs) self._compareBothSparse(x, np.negative, tf.neg) self._compareBothSparse(x, np.square, tf.square) self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3) self._compareBothSparse(x, np.tanh, tf.tanh) self._compareBothSparse(y, np.sign, tf.sign) self._compareBothSparse(x, np.vectorize(math.erf), tf.erf, tol=1e-3)
Example #8
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testComplex64Basic(self): x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype( np.complex64) y = x + 0.5 # no zeros self._compareCpu(x, np.abs, tf.complex_abs) self._compareCpu(x, np.abs, _ABS) self._compareCpu(x, np.negative, tf.neg) self._compareCpu(x, np.negative, _NEG) self._compareCpu(y, self._inv, tf.inv) self._compareCpu(x, np.square, tf.square) self._compareCpu(y, np.sqrt, tf.sqrt) self._compareCpu(y, self._rsqrt, tf.rsqrt) self._compareCpu(x, np.exp, tf.exp) self._compareCpu(y, np.log, tf.log) self._compareCpu(y, np.log1p, tf.log1p) self._compareCpu(x, np.tanh, tf.tanh) self._compareCpu(x, self._sigmoid, tf.sigmoid) self._compareCpu(x, np.sin, tf.sin) self._compareCpu(x, np.cos, tf.cos) self._compareBothSparse(x, np.abs, tf.abs) self._compareBothSparse(x, np.negative, tf.neg) self._compareBothSparse(x, np.square, tf.square) self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3) self._compareBothSparse(x, np.tanh, tf.tanh) # Numpy uses an incorrect definition of sign; use the right one instead. def complex_sign(x): return x / np.abs(x) self._compareCpu(y, complex_sign, tf.sign) self._compareBothSparse(y, complex_sign, tf.sign)
Example #9
Source File: custom_patch.py From tensorbuilder with MIT License | 4 votes |
def drop_layer(x, keep_prob, seed=None, name=None): """Computes dropout. With probability `keep_prob`, outputs the input element scaled up by `1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. Args: x: A tensor. keep_prob: A scalar `Tensor` with the same type as x. The probability that each element is kept. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed) for behavior. name: A name for this operation (optional). Returns: A Tensor of the same shape of `x`. Raises: ValueError: If `keep_prob` is not in `(0, 1]`. """ with tf.op_scope([x], name, "drop_layer") as name: x = tf.convert_to_tensor(x, name="x") if isinstance(keep_prob, float) and not 0 < keep_prob <= 1: raise ValueError("keep_prob must be a scalar tensor or a float in the " "range (0, 1], got %g" % keep_prob) keep_prob = tf.convert_to_tensor(keep_prob, dtype=x.dtype, name="keep_prob") keep_prob.get_shape().assert_is_compatible_with(tensor_shape.scalar()) noise_shape = [ tf.shape(x)[0], 1 ] # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += tf.random_uniform( noise_shape, seed=seed, dtype=x.dtype ) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = tf.floor(random_tensor) ret = x * tf.inv(keep_prob) * binary_tensor ret.set_shape(x.get_shape()) return ret