Python tensorflow.floordiv() Examples
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Example #1
Source File: discretization.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
def int_to_bit(x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) x_labels = [] for i in range(num_bits): x_labels.append( tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base))) res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #2
Source File: feedback.py From sequencing with MIT License | 6 votes |
def sample(self, logits, time): rl_time_steps = tf.floordiv(tf.maximum(self.global_step_tensor - self.burn_in_step, 0), self.increment_step) start_rl_step = self.sequence_length - rl_time_steps next_input_ids = tf.cond( tf.greater_equal(time, self.max_sequence_length), lambda: tf.tile([self.eos_id], [self.batch_size]), lambda: self._input_tas.read(time)) next_predicted_ids = tf.squeeze(tf.multinomial(logits, 1), axis=[-1]) mask = tf.to_int32(time >= start_rl_step) return (1 - mask) * tf.to_int32(next_input_ids) + mask * tf.to_int32( next_predicted_ids)
Example #3
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def _testBCastByFunc(self, funcs, xs, ys): dtypes = [ np.float16, np.float32, np.float64, np.int32, np.int64, np.complex64, np.complex128, ] for dtype in dtypes: for (np_func, tf_func) in funcs: if (dtype in (np.complex64, np.complex128) and tf_func in (_FLOORDIV, tf.floordiv)): continue # floordiv makes no sense for complex numbers self._compareBCast(xs, ys, dtype, np_func, tf_func) self._compareBCast(ys, xs, dtype, np_func, tf_func)
Example #4
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def _compareBCast(self, xs, ys, dtype, np_func, tf_func): if dtype in (np.complex64, np.complex128): x = (1 + np.linspace(0, 2 + 3j, np.prod(xs))).astype(dtype).reshape(xs) y = (1 + np.linspace(0, 2 - 2j, np.prod(ys))).astype(dtype).reshape(ys) else: x = (1 + np.linspace(0, 5, np.prod(xs))).astype(dtype).reshape(xs) y = (1 + np.linspace(0, 5, np.prod(ys))).astype(dtype).reshape(ys) self._compareCpu(x, y, np_func, tf_func) if x.dtype in (np.float16, np.float32, np.float64, np.complex64, np.complex128): if tf_func not in (_FLOORDIV, tf.floordiv): if x.dtype == np.float16: # Compare fp16 theoretical gradients to fp32 numerical gradients, # since fp16 numerical gradients are too imprecise unless great # care is taken with choosing the inputs and the delta. This is # a weaker check (in particular, it does not test the op itself, # only its gradient), but it's much better than nothing. self._compareGradientX(x, y, np_func, tf_func, np.float) self._compareGradientY(x, y, np_func, tf_func, np.float) else: self._compareGradientX(x, y, np_func, tf_func) self._compareGradientY(x, y, np_func, tf_func) self._compareGpu(x, y, np_func, tf_func) # TODO(josh11b,vrv): Refactor this to use parameterized tests.
Example #5
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testInt32Basic(self): x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int32) y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int32) self._compareBoth(x, y, np.add, tf.add) self._compareBoth(x, y, np.subtract, tf.sub) self._compareBoth(x, y, np.multiply, tf.mul) self._compareBoth(x, y, np.true_divide, tf.truediv) self._compareBoth(x, y, np.floor_divide, tf.floordiv) self._compareBoth(x, y, np.mod, tf.mod) self._compareBoth(x, y, np.add, _ADD) self._compareBoth(x, y, np.subtract, _SUB) self._compareBoth(x, y, np.multiply, _MUL) self._compareBoth(x, y, np.true_divide, _TRUEDIV) self._compareBoth(x, y, np.floor_divide, _FLOORDIV) self._compareBoth(x, y, np.mod, _MOD) # _compareBoth tests on GPU only for floating point types, so test # _MOD for int32 on GPU by calling _compareGpu self._compareGpu(x, y, np.mod, _MOD)
Example #6
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testDoubleBasic(self): x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float64) y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float64) self._compareBoth(x, y, np.add, tf.add) self._compareBoth(x, y, np.subtract, tf.sub) self._compareBoth(x, y, np.multiply, tf.mul) self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv) self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv) self._compareBoth(x, y, np.add, _ADD) self._compareBoth(x, y, np.subtract, _SUB) self._compareBoth(x, y, np.multiply, _MUL) self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV) self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV) try: from scipy import special # pylint: disable=g-import-not-at-top a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32) x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32) self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma) self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac) except ImportError as e: tf.logging.warn("Cannot test special functions: %s" % str(e))
Example #7
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testFloatBasic(self): x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float32) y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float32) self._compareBoth(x, y, np.add, tf.add, also_compare_variables=True) self._compareBoth(x, y, np.subtract, tf.sub) self._compareBoth(x, y, np.multiply, tf.mul) self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv) self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv) self._compareBoth(x, y, np.add, _ADD) self._compareBoth(x, y, np.subtract, _SUB) self._compareBoth(x, y, np.multiply, _MUL) self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV) self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV) try: from scipy import special # pylint: disable=g-import-not-at-top a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32) x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32) self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma) self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac) # Need x > 1 self._compareBoth(x_pos_small + 1, a_pos_small, special.zeta, tf.zeta) n_small = np.arange(0, 15).reshape(1, 3, 5).astype(np.float32) self._compareBoth(n_small, x_pos_small, special.polygamma, tf.polygamma) except ImportError as e: tf.logging.warn("Cannot test special functions: %s" % str(e))
Example #8
Source File: preprocessing.py From Real-time-self-adaptive-deep-stereo with Apache License 2.0 | 6 votes |
def pad_image(immy,down_factor = 256,dynamic=False): """ pad image with a proper number of 0 to prevent problem when concatenating after upconv Args: immy: metaop that produces an image down_factor: downgrade resolution that should be respected before feeding the image to the network dynamic: if dynamic is True use dynamic shape of immy, otherway use static shape """ if dynamic: immy_shape = tf.shape(immy) new_height = tf.where(tf.equal(immy_shape[-3]%down_factor,0),x=immy_shape[-3],y=(tf.floordiv(immy_shape[-3],down_factor)+1)*down_factor) new_width = tf.where(tf.equal(immy_shape[-2]%down_factor,0),x=immy_shape[-2],y=(tf.floordiv(immy_shape[-2],down_factor)+1)*down_factor) else: immy_shape = immy.get_shape().as_list() new_height = immy_shape[-3] if immy_shape[-3]%down_factor==0 else ((immy_shape[-3]//down_factor)+1)*down_factor new_width = immy_shape[-2] if immy_shape[-2]%down_factor==0 else ((immy_shape[-2]//down_factor)+1)*down_factor pad_height_left = (new_height-immy_shape[-3])//2 pad_height_right = (new_height-immy_shape[-3]+1)//2 pad_width_left = (new_width-immy_shape[-2])//2 pad_width_right = (new_width-immy_shape[-2]+1)//2 immy = tf.pad(immy,[[0,0],[pad_height_left,pad_height_right],[pad_width_left,pad_width_right],[0,0]],mode="REFLECT") return immy
Example #9
Source File: discretization.py From acai with Apache License 2.0 | 6 votes |
def int_to_bit(self, x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) x_labels = [] for i in range(num_bits): x_labels.append( tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base) ** i), tf.to_int32(base))) res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #10
Source File: vq_discrete.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
def int_to_bit(self, x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) x_labels = [] for i in range(num_bits): x_labels.append( tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base))) res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #11
Source File: ops.py From listen-attend-and-spell with Apache License 2.0 | 6 votes |
def pyramidal_stack(outputs, sequence_length): shape = tf.shape(outputs) batch_size, max_time = shape[0], shape[1] num_units = outputs.get_shape().as_list()[-1] paddings = [[0, 0], [0, tf.floormod(max_time, 2)], [0, 0]] outputs = tf.pad(outputs, paddings) ''' even_time = outputs[:, ::2, :] odd_time = outputs[:, 1::2, :] concat_outputs = tf.concat([even_time, odd_time], -1) ''' concat_outputs = tf.reshape(outputs, (batch_size, -1, num_units * 2)) return concat_outputs, tf.floordiv(sequence_length, 2) + tf.floormod(sequence_length, 2)
Example #12
Source File: discretization.py From BERT with Apache License 2.0 | 6 votes |
def int_to_bit(x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) x_labels = [tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base)) for i in range(num_bits)] res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #13
Source File: vq_discrete.py From BERT with Apache License 2.0 | 6 votes |
def int_to_bit(self, x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) # pylint: disable=g-complex-comprehension x_labels = [ tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base)) for i in range(num_bits)] res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #14
Source File: preprocessing.py From Learning2AdaptForStereo with Apache License 2.0 | 6 votes |
def pad_image(immy,down_factor = 256,dynamic=False): """ pad image with a proper number of 0 to prevent problem when concatenating after upconv Args: immy: metaop that produces an image down_factor: downgrade resolution that should be respected before feeding the image to the network dynamic: if dynamic is True use dynamic shape of immy, otherway use static shape """ if dynamic: immy_shape = tf.shape(immy) new_height = tf.where(tf.equal(immy_shape[-3]%down_factor,0),x=immy_shape[-3],y=(tf.floordiv(immy_shape[-3],down_factor)+1)*down_factor) new_width = tf.where(tf.equal(immy_shape[-2]%down_factor,0),x=immy_shape[-2],y=(tf.floordiv(immy_shape[-2],down_factor)+1)*down_factor) else: immy_shape = immy.get_shape().as_list() new_height = immy_shape[-3] if immy_shape[-3]%down_factor==0 else ((immy_shape[-3]//down_factor)+1)*down_factor new_width = immy_shape[-2] if immy_shape[-2]%down_factor==0 else ((immy_shape[-2]//down_factor)+1)*down_factor pad_height_left = (new_height-immy_shape[-3])//2 pad_height_right = (new_height-immy_shape[-3]+1)//2 pad_width_left = (new_width-immy_shape[-2])//2 pad_width_right = (new_width-immy_shape[-2]+1)//2 immy = tf.pad(immy,[[0,0],[pad_height_left,pad_height_right],[pad_width_left,pad_width_right],[0,0]],mode="REFLECT") return immy
Example #15
Source File: discretization.py From fine-lm with MIT License | 6 votes |
def int_to_bit(x_int, num_bits, base=2): """Turn x_int representing numbers into a bitwise (lower-endian) tensor. Args: x_int: Tensor containing integer to be converted into base notation. num_bits: Number of bits in the representation. base: Base of the representation. Returns: Corresponding number expressed in base. """ x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1)) x_labels = [] for i in range(num_bits): x_labels.append( tf.floormod( tf.floordiv(tf.to_int32(x_l), tf.to_int32(base)**i), tf.to_int32(base))) res = tf.concat(x_labels, axis=-1) return tf.to_float(res)
Example #16
Source File: tensorflow_backend.py From keras-contrib with MIT License | 5 votes |
def extract_image_patches(x, ksizes, ssizes, padding='same', data_format='channels_last'): """Extract the patches from an image. # Arguments x: The input image ksizes: 2-d tuple with the kernel size ssizes: 2-d tuple with the strides size padding: 'same' or 'valid' data_format: 'channels_last' or 'channels_first' # Returns The (k_w,k_h) patches extracted TF ==> (batch_size,w,h,k_w,k_h,c) TH ==> (batch_size,w,h,c,k_w,k_h) """ kernel = [1, ksizes[0], ksizes[1], 1] strides = [1, ssizes[0], ssizes[1], 1] padding = _preprocess_padding(padding) if data_format == 'channels_first': x = K.permute_dimensions(x, (0, 2, 3, 1)) bs_i, w_i, h_i, ch_i = K.int_shape(x) patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1], padding) # Reshaping to fit Theano bs, w, h, ch = K.int_shape(patches) reshaped = tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i]) final_shape = [-1, w, h, ch_i, ksizes[0], ksizes[1]] patches = tf.reshape(tf.transpose(reshaped, [0, 1, 2, 4, 3]), final_shape) if data_format == 'channels_last': patches = K.permute_dimensions(patches, [0, 1, 2, 4, 5, 3]) return patches
Example #17
Source File: model.py From mac-network with Apache License 2.0 | 5 votes |
def initTowerBatch(self, towerI, towersNum, dataSize): towerBatchSize = tf.floordiv(dataSize, towersNum) start = towerI * towerBatchSize end = (towerI + 1) * towerBatchSize if towerI < towersNum - 1 else dataSize self.questionsIndices = self.questionsIndicesAll[start:end] self.questionLengths = self.questionLengthsAll[start:end] self.images = self.imagesAll[start:end] self.answersIndices = self.answersIndicesAll[start:end] self.batchSize = end - start
Example #18
Source File: ops.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 5 votes |
def fpn_feature_levels(num_levels, unit_scale_index, image_ratio, boxes): """Returns fpn feature level for each box based on its area. See section 4.2 of https://arxiv.org/pdf/1612.03144.pdf for details. Args: num_levels: An integer indicating the number of feature levels to crop boxes from. unit_scale_index: An 0-based integer indicating the index of feature map which most closely matches the resolution of the pretrained model. image_ratio: A float indicating the ratio of input image area to pretraining image area. boxes: A float tensor of shape [batch, num_boxes, 4] containing boxes of the form [ymin, xmin, ymax, xmax] in normalized coordinates. Returns: An int32 tensor of shape [batch_size, num_boxes] containing feature indices. """ assert num_levels > 0, ( '`num_levels` must be > 0. Found {}'.format(num_levels)) assert unit_scale_index < num_levels and unit_scale_index >= 0, ( '`unit_scale_index` must be in [0, {}). Found {}.'.format( num_levels, unit_scale_index)) box_height_width = boxes[:, :, 2:4] - boxes[:, :, 0:2] areas_sqrt = tf.sqrt(tf.reduce_prod(box_height_width, axis=2)) log_2 = tf.cast(tf.log(2.0), dtype=boxes.dtype) levels = tf.cast( tf.floordiv(tf.log(areas_sqrt * image_ratio), log_2) + unit_scale_index, dtype=tf.int32) levels = tf.maximum(0, tf.minimum(num_levels - 1, levels)) return levels
Example #19
Source File: model.py From lcgn with BSD 2-Clause "Simplified" License | 5 votes |
def init_tower_batch(self, towerI, towersNum, dataSize): towerBatchSize = tf.floordiv(dataSize, towersNum) start = towerI * towerBatchSize end = (towerI+1)*towerBatchSize if towerI < towersNum-1 else dataSize self.questionIndices = self.questionIndicesAll[start:end] self.questionLengths = self.questionLengthsAll[start:end] self.images = self.imagesAll[start:end] self.imagesObjectNum = self.imagesObjectNumAll[start:end] if cfg.BUILD_VQA: self.answerIndices = self.answerIndicesAll[start:end] if cfg.BUILD_REF: self.bboxIndGt = self.bboxIndGtAll[start:end] self.bboxOffsetGt = self.bboxOffsetGtAll[start:end] self.batchSize = end - start
Example #20
Source File: model.py From lcgn with BSD 2-Clause "Simplified" License | 5 votes |
def init_tower_batch(self, towerI, towersNum, dataSize): towerBatchSize = tf.floordiv(dataSize, towersNum) start = towerI * towerBatchSize end = (towerI+1)*towerBatchSize if towerI < towersNum-1 else dataSize self.questionIndices = self.questionIndicesAll[start:end] self.questionLengths = self.questionLengthsAll[start:end] self.images = self.imagesAll[start:end] self.imagesObjectNum = self.imagesObjectNumAll[start:end] self.answerIndices = self.answerIndicesAll[start:end] self.batchSize = end - start
Example #21
Source File: seq2seq_helpers.py From DeepDeepParser with Apache License 2.0 | 5 votes |
def gather_forced_att_logits(encoder_input_symbols, encoder_decoder_vocab_map, att_logit, batch_size, attn_length, target_vocab_size): """Gathers attention weights as logits for forced attention.""" flat_input_symbols = tf.reshape(encoder_input_symbols, [-1]) flat_label_symbols = tf.gather(encoder_decoder_vocab_map, flat_input_symbols) flat_att_logits = tf.reshape(att_logit, [-1]) flat_range = tf.to_int64(tf.range(tf.shape(flat_label_symbols)[0])) batch_inds = tf.floordiv(flat_range, attn_length) position_inds = tf.mod(flat_range, attn_length) attn_vocab_inds = tf.transpose(tf.pack( [batch_inds, position_inds, tf.to_int64(flat_label_symbols)])) # Exclude indexes of entries with flat_label_symbols[i] = -1. included_flat_indexes = tf.reshape(tf.where(tf.not_equal( flat_label_symbols, -1)), [-1]) included_attn_vocab_inds = tf.gather(attn_vocab_inds, included_flat_indexes) included_flat_att_logits = tf.gather(flat_att_logits, included_flat_indexes) sparse_shape = tf.to_int64(tf.pack( [batch_size, attn_length, target_vocab_size])) sparse_label_logits = tf.SparseTensor(included_attn_vocab_inds, included_flat_att_logits, sparse_shape) forced_att_logit_sum = tf.sparse_reduce_sum(sparse_label_logits, [1]) forced_att_logit = tf.reshape(forced_att_logit_sum, [-1, target_vocab_size]) return forced_att_logit
Example #22
Source File: det_tools.py From hfnet with MIT License | 5 votes |
def extract_xy_coords(d_heatmaps, block_size): batch = tf.shape(d_heatmaps)[0] rheight = tf.shape(d_heatmaps)[1] rwidth = tf.shape(d_heatmaps)[2] width = rwidth * block_size height = rheight * block_size d_argmax = tf.cast(tf.argmax(d_heatmaps, axis=-1), dtype=tf.int32) fgmask = tf.cast(tf.not_equal(d_argmax, block_size**2), dtype=tf.int32) x_bcoords = tf.mod(d_argmax, block_size) y_bcoords = tf.floordiv(d_argmax, block_size) # floor_div ? zero = tf.constant(0, dtype=tf.int32) zeros = tf.zeros_like(x_bcoords) x_bcoords = tf.where(tf.equal(fgmask, zero), zeros, x_bcoords) y_bcoords = tf.where(tf.equal(fgmask, zero), zeros, y_bcoords) x_offset, y_offset = tf.meshgrid(tf.range(0, width, block_size), tf.range(0, height, block_size)) x_offset = tf.tile(tf.expand_dims(x_offset, axis=0), [batch, 1, 1]) y_offset = tf.tile(tf.expand_dims(y_offset, axis=0), [batch, 1, 1]) x_icoords = x_bcoords + x_offset y_icoords = y_bcoords + y_offset return x_icoords, y_icoords, fgmask
Example #23
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testOverload(self): dtypes = [ tf.float16, tf.float32, tf.float64, tf.int32, tf.int64, tf.complex64, tf.complex128, ] funcs = [ (np.add, _ADD), (np.subtract, _SUB), (np.multiply, _MUL), (np.power, _POW), (np.true_divide, _TRUEDIV), (np.floor_divide, _FLOORDIV), ] for dtype in dtypes: for np_func, tf_func in funcs: if dtype in (tf.complex64, tf.complex128) and tf_func == _FLOORDIV: continue # floordiv makes no sense for complex self._compareBinary(10, 5, dtype, np_func, tf_func) # Mod only works for int32 and int64. for dtype in [tf.int32, tf.int64]: self._compareBinary(10, 3, dtype, np.mod, _MOD)
Example #24
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def _testBCastD(self, xs, ys): funcs = [ (np.true_divide, tf.truediv), (np.floor_divide, tf.floordiv), (np.true_divide, _TRUEDIV), (np.floor_divide, _FLOORDIV), ] self._testBCastByFunc(funcs, xs, ys)
Example #25
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testUint16Basic(self): x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint16) y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint16) self._compareBoth(x, y, np.multiply, tf.mul) self._compareBoth(x, y, np.multiply, _MUL) self._compareBoth(x, y, np.true_divide, tf.truediv) self._compareBoth(x, y, np.floor_divide, tf.floordiv) self._compareBoth(x, y, np.true_divide, _TRUEDIV) self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
Example #26
Source File: cwise_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def _compareBoth(self, x, y, np_func, tf_func, also_compare_variables=False): self._compareCpu(x, y, np_func, tf_func, also_compare_variables) if x.dtype in (np.float16, np.float32, np.float64): if tf_func not in (_FLOORDIV, tf.floordiv, tf.igamma, tf.igammac, tf.zeta, tf.polygamma): self._compareGradientX(x, y, np_func, tf_func) self._compareGradientY(x, y, np_func, tf_func) if tf_func in (tf.igamma, tf.igammac, tf.zeta, tf.polygamma): # These methods only support gradients in the second parameter self._compareGradientY(x, y, np_func, tf_func) self._compareGpu(x, y, np_func, tf_func)
Example #27
Source File: swa_train_cpn.py From tf.fashionAI with Apache License 2.0 | 5 votes |
def get_keypoint(image, targets, predictions, heatmap_size, height, width, category, clip_at_zero=True, data_format='channels_last', name=None): predictions = tf.reshape(predictions, [1, -1, heatmap_size*heatmap_size]) pred_max = tf.reduce_max(predictions, axis=-1) pred_indices = tf.argmax(predictions, axis=-1) pred_x, pred_y = tf.cast(tf.floormod(pred_indices, heatmap_size), tf.float32), tf.cast(tf.floordiv(pred_indices, heatmap_size), tf.float32) width, height = tf.cast(width, tf.float32), tf.cast(height, tf.float32) pred_x, pred_y = pred_x * width / tf.cast(heatmap_size, tf.float32), pred_y * height / tf.cast(heatmap_size, tf.float32) if clip_at_zero: pred_x, pred_y = pred_x * tf.cast(pred_max>0, tf.float32), pred_y * tf.cast(pred_max>0, tf.float32) pred_x = pred_x * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (width / 2.) pred_y = pred_y * tf.cast(pred_max>0, tf.float32) + tf.cast(pred_max<=0, tf.float32) * (height / 2.) if config.PRED_DEBUG: pred_indices_ = tf.squeeze(pred_indices) image_ = tf.squeeze(image) * 255. pred_heatmap = tf.one_hot(pred_indices_, heatmap_size*heatmap_size, on_value=1., off_value=0., axis=-1, dtype=tf.float32) pred_heatmap = tf.reshape(pred_heatmap, [-1, heatmap_size, heatmap_size]) if data_format == 'channels_first': image_ = tf.transpose(image_, perm=(1, 2, 0)) save_image_op = tf.py_func(save_image_with_heatmap, [image_, height, width, heatmap_size, tf.reshape(pred_heatmap * 255., [-1, heatmap_size, heatmap_size]), tf.reshape(predictions, [-1, heatmap_size, heatmap_size]), config.left_right_group_map[category][0], config.left_right_group_map[category][1], config.left_right_group_map[category][2]], tf.int64, stateful=True) with tf.control_dependencies([save_image_op]): pred_x, pred_y = pred_x * 1., pred_y * 1. return pred_x, pred_y
Example #28
Source File: keras_contrib_backend.py From se_relativisticgan with MIT License | 5 votes |
def extract_image_patches(x, ksizes, ssizes, padding='same', data_format='channels_last'): ''' Extract the patches from an image # Parameters x : The input image ksizes : 2-d tuple with the kernel size ssizes : 2-d tuple with the strides size padding : 'same' or 'valid' data_format : 'channels_last' or 'channels_first' # Returns The (k_w,k_h) patches extracted TF ==> (batch_size,w,h,k_w,k_h,c) TH ==> (batch_size,w,h,c,k_w,k_h) ''' kernel = [1, ksizes[0], ksizes[1], 1] strides = [1, ssizes[0], ssizes[1], 1] padding = _preprocess_padding(padding) if data_format == 'channels_first': x = KTF.permute_dimensions(x, (0, 2, 3, 1)) bs_i, w_i, h_i, ch_i = KTF.int_shape(x) patches = tf.extract_image_patches(x, kernel, strides, [1, 1, 1, 1], padding) # Reshaping to fit Theano bs, w, h, ch = KTF.int_shape(patches) patches = tf.reshape(tf.transpose(tf.reshape(patches, [-1, w, h, tf.floordiv(ch, ch_i), ch_i]), [0, 1, 2, 4, 3]), [-1, w, h, ch_i, ksizes[0], ksizes[1]]) if data_format == 'channels_last': patches = KTF.permute_dimensions(patches, [0, 1, 2, 4, 5, 3]) return patches
Example #29
Source File: preprocessing.py From finetune_classification with Apache License 2.0 | 5 votes |
def _border_expand(image, mode='CONSTANT', constant_values=255): """Expands the given image. Args: Args: image: A 3-D image `Tensor`. output_height: The height of the image after Expanding. output_width: The width of the image after Expanding. resize: A boolean indicating whether to resize the expanded image to [output_height, output_width, channels] or not. Returns: expanded_image: A 3-D tensor containing the resized image. """ shape = tf.shape(image) height = shape[0] width = shape[1] def _pad_left_right(): pad_left = tf.floordiv(height - width, 2) pad_right = height - width - pad_left return [[0, 0], [pad_left, pad_right], [0, 0]] def _pad_top_bottom(): pad_top = tf.floordiv(width - height, 2) pad_bottom = width - height - pad_top return [[pad_top, pad_bottom], [0, 0], [0, 0]] paddings = tf.cond(tf.greater(height, width), _pad_left_right, _pad_top_bottom) expanded_image = tf.pad(image, paddings, mode=mode, constant_values=constant_values) return expanded_image
Example #30
Source File: ops.py From tfdeploy with MIT License | 5 votes |
def test_FloorDiv(self): t = tf.floordiv(*self.random((3, 5), (3, 5))) self.check(t)