# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # #Licensed under the Apache License, Version 2.0 (the "License"); #you may not use this file except in compliance with the License. #You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. import sys import unittest import numpy as np from op_test import OpTest from test_softmax_op import stable_softmax CUDA_BLOCK_SIZE = 512 class CTCForward(object): def __init__(self, softmax, softmax_lod, labels, labels_lod, blank, norm_by_times): self.softmax = softmax self.softmax_lod = softmax_lod assert labels.shape[1] == 1 self.labels = labels self.labels_lod = labels_lod self.blank = blank self.norm_by_times = norm_by_times self.level = 0 self.num_classes = softmax.shape[1] self.batch_size = len(softmax_lod[self.level]) - 1 assert self.batch_size == len(labels_lod[self.level]) - 1 self.loss = np.zeros([self.batch_size, 1], dtype="float32") self.gradient = np.zeros(self.softmax.shape, dtype="float32") # float64 self.EXP_MAX = sys.float_info.max self.EXP_MIN = sys.float_info.min self.LOG_ZERO = np.log(self.EXP_MIN) self.LOG_INFINITY = np.log(self.EXP_MAX) def safe_exp(self, x): if x <= self.LOG_ZERO: return 0.0 if x >= self.LOG_INFINITY: return self.EXP_MAX return np.exp(x) def safe_log(self, x): if x <= self.EXP_MIN: return self.LOG_ZERO return np.log(x) # x = lna and y = lnb are in log scale, ln(a / b) = lna - lnb def log_div(self, x, y): res = x - y if res <= self.LOG_ZERO: return self.LOG_ZERO if res >= self.LOG_INFINITY: return self.LOG_INFINITY return res # x = lna and y = lnb are in log scale, ln(a * b) = lna + lnb def log_mul(self, x, y): res = x + y if res <= self.LOG_ZERO: return self.LOG_ZERO if res >= self.LOG_INFINITY: return self.LOG_INFINITY return res # x = lna and y = lnb are in log scale, # ln(a + b) = lna + ln(1 + exp(lnb - lna)), where b > a def log_add(self, x, y): if x < y: t = y y = x x = t return x + self.safe_log(1 + self.safe_exp(y - x)) def segment_range(self, time, total_times, total_segments): start = max(0, total_segments - (2 * (total_times - time))) end = min(total_segments, 2 * (time + 1)) return start, end def forward_a_sequence(self, softmax_a_sequence, labels_a_sequence): total_times = softmax_a_sequence.shape[0] total_segments = labels_a_sequence.shape[0] * 2 + 1 required_times = labels_a_sequence.shape[0] old_label = -1 for i in range(labels_a_sequence.shape[0]): # two contingous labels with the same value if labels_a_sequence[i, 0] == old_label: required_times = required_times + 1 old_label = labels_a_sequence[i, 0] if total_times < required_times: return 0 # calculate the forward and backward variables, # reference Chapter 7.3 of "Alex Grave, Supervised Sequence # Labelling with Recurrent Neural Networks" log_acts = np.zeros([total_times, self.num_classes], dtype="float32") for i in range(total_times): for j in range(self.num_classes): log_acts[i, j] = self.safe_log(softmax_a_sequence[i, j]) # calculate the forward variables forward_vars = np.zeros([total_times, total_segments], dtype="float32") for i in range(total_times): for j in range(total_segments): forward_vars[i, j] = self.LOG_ZERO for i in range(total_times): # dp initialization at t0 if i == 0: forward_vars[i, 0] = log_acts[0, self.blank] if total_segments > 1: forward_vars[i, 1] = log_acts[0, labels_a_sequence[i, 0]] continue # dp from t1 start, end = self.segment_range(i, total_times, total_segments) for k in range(end - start): j = k + start if j & 1 == 1: label_idx = j / 2 label_val = labels_a_sequence[label_idx, 0] fv = self.log_add(forward_vars[i - 1, j], forward_vars[i - 1, j - 1]) if j > 1 and label_val != labels_a_sequence[label_idx - 1, 0]: fv = self.log_add(fv, forward_vars[i - 1, j - 2]) fv = self.log_mul(fv, log_acts[i, label_val]) else: fv = forward_vars[i - 1, j] if j > 0: fv = self.log_add(fv, forward_vars[i - 1, j - 1]) fv = self.log_mul(fv, log_acts[i, self.blank]) forward_vars[i, j] = fv # sum the last two value as log_prob log_prob = forward_vars[total_times - 1, total_segments - 1] if total_segments > 1: log_prob = self.log_add( log_prob, forward_vars[total_times - 1, total_segments - 2]) return -log_prob def forward(self): for i in range(self.batch_size): softmax_start_i = self.softmax_lod[self.level][i] softmax_end_i = self.softmax_lod[self.level][i + 1] labels_start_i = self.labels_lod[self.level][i] labels_end_i = self.labels_lod[self.level][i + 1] softmax_a_sequence = self.softmax[softmax_start_i:softmax_end_i, :] labels_a_sequence = self.labels[labels_start_i:labels_end_i, :] self.loss[i] = self.forward_a_sequence(softmax_a_sequence, labels_a_sequence) return self.loss class TestWarpCTCOp(OpTest): def config(self): self.batch_size = 4 self.num_classes = 8 self.logits_lod = [[0, 4, 5, 8, 11]] self.labels_lod = [[0, 3, 4, 8, 12]] self.blank = self.num_classes - 1 self.norm_by_times = False def setUp(self): self.op_type = "warpctc" self.config() logits = np.random.uniform( 0.1, 1.0, [self.logits_lod[0][-1], self.num_classes]).astype("float32") softmax = np.apply_along_axis(stable_softmax, 1, logits) # labels should not be blank labels = np.random.randint( 0, self.num_classes - 1, [self.labels_lod[0][-1], 1], dtype="int32") ctc = CTCForward(softmax, self.logits_lod, labels, self.labels_lod, self.blank, self.norm_by_times) loss = ctc.forward() max_sequence_length = 0 for i in range(self.batch_size): max_sequence_length = max( max_sequence_length, self.logits_lod[0][i + 1] - self.logits_lod[0][i]) self.gradient = np.zeros( [max_sequence_length, self.batch_size, self.num_classes], dtype="float32") self.inputs = { "Logits": (logits, self.logits_lod), "Label": (labels, self.labels_lod) } self.outputs = {"Loss": loss} self.attrs = {"blank": self.blank, "norm_by_times": self.norm_by_times} def test_check_output(self): self.check_output() def test_check_grad(self): self.outputs['WarpCTCGrad'] = self.gradient self.check_grad(["Logits"], "Loss", max_relative_error=0.007) class TestWarpCTCOpCase1(TestWarpCTCOp): def config(self): self.batch_size = 4 self.num_classes = CUDA_BLOCK_SIZE + 2 self.logits_lod = [[0, 4, 5, 8, 11]] self.labels_lod = [[0, 3, 4, 8, 12]] self.blank = 0 self.norm_by_times = False if __name__ == "__main__": unittest.main()