diff --git a/ppocr/modeling/backbones/rec_efficientb3_pren.py b/ppocr/modeling/backbones/rec_efficientb3_pren.py index 57eef178869fc7f5ff55b3548674c741fb4f3ead..701e436c1e0e29f42cc9c7ce6e66552d4005f6b0 100644 --- a/ppocr/modeling/backbones/rec_efficientb3_pren.py +++ b/ppocr/modeling/backbones/rec_efficientb3_pren.py @@ -21,124 +21,165 @@ from __future__ import division from __future__ import print_function import math -from collections import namedtuple +import re +import collections import paddle import paddle.nn as nn import paddle.nn.functional as F __all__ = ['EfficientNetb3'] +GlobalParams = collections.namedtuple('GlobalParams', [ + 'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'num_classes', + 'width_coefficient', 'depth_coefficient', 'depth_divisor', 'min_depth', + 'drop_connect_rate', 'image_size' +]) -class EffB3Params: +BlockArgs = collections.namedtuple('BlockArgs', [ + 'kernel_size', 'num_repeat', 'input_filters', 'output_filters', + 'expand_ratio', 'id_skip', 'stride', 'se_ratio' +]) + + +class BlockDecoder: @staticmethod - def get_global_params(): - """ - The fllowing are efficientnetb3's arch superparams, but to fit for scene - text recognition task, the resolution(image_size) here is changed - from 300 to 64. - """ - GlobalParams = namedtuple('GlobalParams', [ - 'drop_connect_rate', 'width_coefficient', 'depth_coefficient', - 'depth_divisor', 'image_size' - ]) - global_params = GlobalParams( - drop_connect_rate=0.3, - width_coefficient=1.2, - depth_coefficient=1.4, - depth_divisor=8, - image_size=64) - return global_params + def _decode_block_string(block_string): + assert isinstance(block_string, str) + + ops = block_string.split('_') + options = {} + for op in ops: + splits = re.split(r'(\d.*)', op) + if len(splits) >= 2: + key, value = splits[:2] + options[key] = value + + assert (('s' in options and len(options['s']) == 1) or + (len(options['s']) == 2 and options['s'][0] == options['s'][1])) + + return BlockArgs( + kernel_size=int(options['k']), + num_repeat=int(options['r']), + input_filters=int(options['i']), + output_filters=int(options['o']), + expand_ratio=int(options['e']), + id_skip=('noskip' not in block_string), + se_ratio=float(options['se']) if 'se' in options else None, + stride=[int(options['s'][0])]) @staticmethod - def get_block_params(): - BlockParams = namedtuple('BlockParams', [ - 'kernel_size', 'num_repeat', 'input_filters', 'output_filters', - 'expand_ratio', 'id_skip', 'se_ratio', 'stride' - ]) - block_params = [ - BlockParams(3, 1, 32, 16, 1, True, 0.25, 1), - BlockParams(3, 2, 16, 24, 6, True, 0.25, 2), - BlockParams(5, 2, 24, 40, 6, True, 0.25, 2), - BlockParams(3, 3, 40, 80, 6, True, 0.25, 2), - BlockParams(5, 3, 80, 112, 6, True, 0.25, 1), - BlockParams(5, 4, 112, 192, 6, True, 0.25, 2), - BlockParams(3, 1, 192, 320, 6, True, 0.25, 1) - ] - return block_params + def decode(string_list): + assert isinstance(string_list, list) + blocks_args = [] + for block_string in string_list: + blocks_args.append(BlockDecoder._decode_block_string(block_string)) + return blocks_args + + +def efficientnet(width_coefficient=None, + depth_coefficient=None, + dropout_rate=0.2, + drop_connect_rate=0.2, + image_size=None, + num_classes=1000): + blocks_args = [ + 'r1_k3_s11_e1_i32_o16_se0.25', + 'r2_k3_s22_e6_i16_o24_se0.25', + 'r2_k5_s22_e6_i24_o40_se0.25', + 'r3_k3_s22_e6_i40_o80_se0.25', + 'r3_k5_s11_e6_i80_o112_se0.25', + 'r4_k5_s22_e6_i112_o192_se0.25', + 'r1_k3_s11_e6_i192_o320_se0.25', + ] + blocks_args = BlockDecoder.decode(blocks_args) + + global_params = GlobalParams( + batch_norm_momentum=0.99, + batch_norm_epsilon=1e-3, + dropout_rate=dropout_rate, + drop_connect_rate=drop_connect_rate, + num_classes=num_classes, + width_coefficient=width_coefficient, + depth_coefficient=depth_coefficient, + depth_divisor=8, + min_depth=None, + image_size=image_size, ) + return blocks_args, global_params class EffUtils: @staticmethod def round_filters(filters, global_params): - """Calculate and round number of filters based on depth multiplier.""" + """ Calculate and round number of filters based on depth multiplier. """ multiplier = global_params.width_coefficient if not multiplier: return filters divisor = global_params.depth_divisor + min_depth = global_params.min_depth filters *= multiplier - new_filters = int(filters + divisor / 2) // divisor * divisor + min_depth = min_depth or divisor + new_filters = max(min_depth, + int(filters + divisor / 2) // divisor * divisor) if new_filters < 0.9 * filters: new_filters += divisor return int(new_filters) @staticmethod def round_repeats(repeats, global_params): - """Round number of filters based on depth multiplier.""" + """ Round number of filters based on depth multiplier. """ multiplier = global_params.depth_coefficient if not multiplier: return repeats return int(math.ceil(multiplier * repeats)) -class ConvBlock(nn.Layer): - def __init__(self, block_params): - super(ConvBlock, self).__init__() - self.block_args = block_params - self.has_se = (self.block_args.se_ratio is not None) and \ - (0 < self.block_args.se_ratio <= 1) - self.id_skip = block_params.id_skip +class MbConvBlock(nn.Layer): + def __init__(self, block_args): + super(MbConvBlock, self).__init__() + self._block_args = block_args + self.has_se = (self._block_args.se_ratio is not None) and \ + (0 < self._block_args.se_ratio <= 1) + self.id_skip = block_args.id_skip # expansion phase - self.input_filters = self.block_args.input_filters - output_filters = \ - self.block_args.input_filters * self.block_args.expand_ratio - if self.block_args.expand_ratio != 1: - self.expand_conv = nn.Conv2D( - self.input_filters, output_filters, 1, bias_attr=False) - self.bn0 = nn.BatchNorm(output_filters) + self.inp = self._block_args.input_filters + oup = self._block_args.input_filters * self._block_args.expand_ratio + if self._block_args.expand_ratio != 1: + self._expand_conv = nn.Conv2D(self.inp, oup, 1, bias_attr=False) + self._bn0 = nn.BatchNorm(oup) # depthwise conv phase - k = self.block_args.kernel_size - s = self.block_args.stride - self.depthwise_conv = nn.Conv2D( - output_filters, - output_filters, - groups=output_filters, + k = self._block_args.kernel_size + s = self._block_args.stride + if isinstance(s, list): + s = s[0] + self._depthwise_conv = nn.Conv2D( + oup, + oup, + groups=oup, kernel_size=k, stride=s, padding='same', bias_attr=False) - self.bn1 = nn.BatchNorm(output_filters) + self._bn1 = nn.BatchNorm(oup) # squeeze and excitation layer, if desired if self.has_se: num_squeezed_channels = max(1, - int(self.block_args.input_filters * - self.block_args.se_ratio)) - self.se_reduce = nn.Conv2D(output_filters, num_squeezed_channels, 1) - self.se_expand = nn.Conv2D(num_squeezed_channels, output_filters, 1) - - # output phase - self.final_oup = self.block_args.output_filters - self.project_conv = nn.Conv2D( - output_filters, self.final_oup, 1, bias_attr=False) - self.bn2 = nn.BatchNorm(self.final_oup) - self.swish = nn.Swish() - - def drop_connect(self, inputs, p, training): + int(self._block_args.input_filters * + self._block_args.se_ratio)) + self._se_reduce = nn.Conv2D(oup, num_squeezed_channels, 1) + self._se_expand = nn.Conv2D(num_squeezed_channels, oup, 1) + + # output phase and some util class + self.final_oup = self._block_args.output_filters + self._project_conv = nn.Conv2D(oup, self.final_oup, 1, bias_attr=False) + self._bn2 = nn.BatchNorm(self.final_oup) + self._swish = nn.Swish() + + def _drop_connect(self, inputs, p, training): if not training: return inputs - batch_size = inputs.shape[0] keep_prob = 1 - p random_tensor = keep_prob @@ -151,22 +192,23 @@ class ConvBlock(nn.Layer): def forward(self, inputs, drop_connect_rate=None): # expansion and depthwise conv x = inputs - if self.block_args.expand_ratio != 1: - x = self.swish(self.bn0(self.expand_conv(inputs))) - x = self.swish(self.bn1(self.depthwise_conv(x))) + if self._block_args.expand_ratio != 1: + x = self._swish(self._bn0(self._expand_conv(inputs))) + x = self._swish(self._bn1(self._depthwise_conv(x))) # squeeze and excitation if self.has_se: x_squeezed = F.adaptive_avg_pool2d(x, 1) - x_squeezed = self.se_expand(self.swish(self.se_reduce(x_squeezed))) + x_squeezed = self._se_expand( + self._swish(self._se_reduce(x_squeezed))) x = F.sigmoid(x_squeezed) * x - x = self.bn2(self.project_conv(x)) + x = self._bn2(self._project_conv(x)) # skip conntection and drop connect - if self.id_skip and self.block_args.stride == 1 and \ - self.input_filters == self.final_oup: + if self.id_skip and self._block_args.stride == 1 and \ + self.inp == self.final_oup: if drop_connect_rate: - x = self.drop_connect( + x = self._drop_connect( x, p=drop_connect_rate, training=self.training) x = x + inputs return x @@ -175,54 +217,63 @@ class ConvBlock(nn.Layer): class EfficientNetb3_PREN(nn.Layer): def __init__(self, in_channels): super(EfficientNetb3_PREN, self).__init__() - self.blocks_params = EffB3Params.get_block_params() - self.global_params = EffB3Params.get_global_params() + """ + the fllowing are efficientnetb3's superparams, + they means efficientnetb3 network's width, depth, resolution and + dropout respectively, to fit for text recognition task, the resolution + here is changed from 300 to 64. + """ + w, d, s, p = 1.2, 1.4, 64, 0.3 + self._blocks_args, self._global_params = efficientnet( + width_coefficient=w, + depth_coefficient=d, + dropout_rate=p, + image_size=s) self.out_channels = [] # stem - stem_channels = EffUtils.round_filters(32, self.global_params) - self.conv_stem = nn.Conv2D( - in_channels, stem_channels, 3, 2, padding='same', bias_attr=False) - self.bn0 = nn.BatchNorm(stem_channels) + out_channels = EffUtils.round_filters(32, self._global_params) + self._conv_stem = nn.Conv2D( + in_channels, out_channels, 3, 2, padding='same', bias_attr=False) + self._bn0 = nn.BatchNorm(out_channels) - self.blocks = [] + # build blocks + self._blocks = [] # to extract three feature maps for fpn based on efficientnetb3 backbone - self.concerned_block_idxes = [7, 17, 25] - concerned_idx = 0 - for i, block_params in enumerate(self.blocks_params): - block_params = block_params._replace( - input_filters=EffUtils.round_filters(block_params.input_filters, - self.global_params), - output_filters=EffUtils.round_filters( - block_params.output_filters, self.global_params), - num_repeat=EffUtils.round_repeats(block_params.num_repeat, - self.global_params)) - self.blocks.append( - self.add_sublayer("{}-0".format(i), ConvBlock(block_params))) - concerned_idx += 1 - if concerned_idx in self.concerned_block_idxes: - self.out_channels.append(block_params.output_filters) - if block_params.num_repeat > 1: - block_params = block_params._replace( - input_filters=block_params.output_filters, stride=1) - for j in range(block_params.num_repeat - 1): - self.blocks.append( - self.add_sublayer('{}-{}'.format(i, j + 1), - ConvBlock(block_params))) - concerned_idx += 1 - if concerned_idx in self.concerned_block_idxes: - self.out_channels.append(block_params.output_filters) - - self.swish = nn.Swish() + self._concerned_block_idxes = [7, 17, 25] + _concerned_idx = 0 + for i, block_args in enumerate(self._blocks_args): + block_args = block_args._replace( + input_filters=EffUtils.round_filters(block_args.input_filters, + self._global_params), + output_filters=EffUtils.round_filters(block_args.output_filters, + self._global_params), + num_repeat=EffUtils.round_repeats(block_args.num_repeat, + self._global_params)) + self._blocks.append( + self.add_sublayer(f"{i}-0", MbConvBlock(block_args))) + _concerned_idx += 1 + if _concerned_idx in self._concerned_block_idxes: + self.out_channels.append(block_args.output_filters) + if block_args.num_repeat > 1: + block_args = block_args._replace( + input_filters=block_args.output_filters, stride=1) + for j in range(block_args.num_repeat - 1): + self._blocks.append( + self.add_sublayer(f'{i}-{j+1}', MbConvBlock(block_args))) + _concerned_idx += 1 + if _concerned_idx in self._concerned_block_idxes: + self.out_channels.append(block_args.output_filters) + + self._swish = nn.Swish() def forward(self, inputs): outs = [] - - x = self.swish(self.bn0(self.conv_stem(inputs))) - for idx, block in enumerate(self.blocks): - drop_connect_rate = self.global_params.drop_connect_rate + x = self._swish(self._bn0(self._conv_stem(inputs))) + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: - drop_connect_rate *= float(idx) / len(self.blocks) + drop_connect_rate *= float(idx) / len(self._blocks) x = block(x, drop_connect_rate=drop_connect_rate) - if idx in self.concerned_block_idxes: + if idx in self._concerned_block_idxes: outs.append(x) return outs diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 749060a053f1442f4bf5df6c5f4b56205e893be8..e8ab4b9f3725d4a0867e93f84c573c2726434391 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -562,7 +562,8 @@ class PRENLabelDecode(BaseRecLabelDecode): return result_list def __call__(self, preds, label=None, *args, **kwargs): - preds = preds.numpy() + if isinstance(preds, paddle.Tensor): + preds = preds.numpy() preds_idx = preds.argmax(axis=2) preds_prob = preds.max(axis=2) text = self.decode(preds_idx, preds_prob) diff --git a/tools/export_model.py b/tools/export_model.py index 8610df83ef08926c245872e711cd1c828eb46765..0797bd726fa9249629557c373774c421e39d5fe6 100755 --- a/tools/export_model.py +++ b/tools/export_model.py @@ -77,7 +77,7 @@ def export_single_model(model, elif arch_config["algorithm"] == "PREN": other_shape = [ paddle.static.InputSpec( - shape=[None, 3, 64, 512], dtype="float32"), + shape=[None, 3, 64, 256], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["model_type"] == "sr": diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index 176e2c68e2c9b2e08f9b56378c45a57733faf8cd..32f66436282e9738e2bf96b72e4a6e056a27df0c 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -100,6 +100,8 @@ class TextRecognizer(object): "use_space_char": args.use_space_char, "rm_symbol": True } + elif self.rec_algorithm == "PREN": + postprocess_params = {'name': 'PRENLabelDecode'} self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors, self.config = \ utility.create_predictor(args, 'rec', logger) @@ -384,7 +386,7 @@ class TextRecognizer(object): self.rec_image_shape) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) - elif self.rec_algorithm == "VisionLAN": + elif self.rec_algorithm in ["VisionLAN", "PREN"]: norm_img = self.resize_norm_img_vl(img_list[indices[ino]], self.rec_image_shape) norm_img = norm_img[np.newaxis, :]