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change argument names according to convention (#131)

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* change argument names according to convention

* bug fix when rename leakyRelu
This commit is contained in:
kang sheng 2021-05-11 16:07:36 +08:00 committed by GitHub
parent 47896a3f80
commit e7f27ae317
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4 changed files with 19 additions and 19 deletions
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2
configs/_base_/recog_models/crnn.py
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@ -4,7 +4,7 @@ label_convertor = dict(
model = dict( model = dict(
type='CRNNNet', type='CRNNNet',
preprocessor=None, preprocessor=None,
backbone=dict(type='VeryDeepVgg', leakyRelu=False), backbone=dict(type='VeryDeepVgg', leaky_relu=False),
encoder=None, encoder=None,
decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True), decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True),
loss=dict(type='CTCLoss', flatten=False), loss=dict(type='CTCLoss', flatten=False),

2
configs/textrecog/crnn/crnn_academic_dataset.py
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@ -21,7 +21,7 @@ label_convertor = dict(
model = dict( model = dict(
type='CRNNNet', type='CRNNNet',
preprocessor=None, preprocessor=None,
backbone=dict(type='VeryDeepVgg', leakyRelu=False, input_channels=1), backbone=dict(type='VeryDeepVgg', leaky_relu=False, input_channels=1),
encoder=None, encoder=None,
decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True), decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True),
loss=dict(type='CTCLoss'), loss=dict(type='CTCLoss'),

32
mmocr/models/textrecog/backbones/very_deep_vgg.py
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@ -9,11 +9,11 @@ class VeryDeepVgg(nn.Module):
"""Implement VGG-VeryDeep backbone for text recognition, modified from """Implement VGG-VeryDeep backbone for text recognition, modified from
`VGG-VeryDeep <https://arxiv.org/pdf/1409.1556.pdf>`_ `VGG-VeryDeep <https://arxiv.org/pdf/1409.1556.pdf>`_
Args: Args:
leaky_relu (bool): Use leakyRelu or not.
input_channels (int): Number of channels of input image tensor. input_channels (int): Number of channels of input image tensor.
leakyRelu (bool): Use leakyRelu or not.
""" """
def __init__(self, leakyRelu=True, input_channels=3): def __init__(self, leaky_relu=True, input_channels=3):
super().__init__() super().__init__()
ks = [3, 3, 3, 3, 3, 3, 2] ks = [3, 3, 3, 3, 3, 3, 2]
@ -25,32 +25,32 @@ class VeryDeepVgg(nn.Module):
cnn = nn.Sequential() cnn = nn.Sequential()
def convRelu(i, batchNormalization=False): def conv_relu(i, batch_normalization=False):
nIn = input_channels if i == 0 else nm[i - 1] n_in = input_channels if i == 0 else nm[i - 1]
nOut = nm[i] n_out = nm[i]
cnn.add_module('conv{0}'.format(i), cnn.add_module('conv{0}'.format(i),
nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i])) nn.Conv2d(n_in, n_out, ks[i], ss[i], ps[i]))
if batchNormalization: if batch_normalization:
cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut)) cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(n_out))
if leakyRelu: if leaky_relu:
cnn.add_module('relu{0}'.format(i), cnn.add_module('relu{0}'.format(i),
nn.LeakyReLU(0.2, inplace=True)) nn.LeakyReLU(0.2, inplace=True))
else: else:
cnn.add_module('relu{0}'.format(i), nn.ReLU(True)) cnn.add_module('relu{0}'.format(i), nn.ReLU(True))
convRelu(0) conv_relu(0)
cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2)) # 64x16x64 cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2)) # 64x16x64
convRelu(1) conv_relu(1)
cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2)) # 128x8x32 cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2)) # 128x8x32
convRelu(2, True) conv_relu(2, True)
convRelu(3) conv_relu(3)
cnn.add_module('pooling{0}'.format(2), cnn.add_module('pooling{0}'.format(2),
nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 256x4x16 nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 256x4x16
convRelu(4, True) conv_relu(4, True)
convRelu(5) conv_relu(5)
cnn.add_module('pooling{0}'.format(3), cnn.add_module('pooling{0}'.format(3),
nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 512x2x16 nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 512x2x16
convRelu(6, True) # 512x1x16 conv_relu(6, True) # 512x1x16
self.cnn = cnn self.cnn = cnn

2
tests/test_models/test_recognizer.py
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@ -27,7 +27,7 @@ def test_base_recognizer():
type='CTCConvertor', dict_file=dict_file, with_unknown=False) type='CTCConvertor', dict_file=dict_file, with_unknown=False)
preprocessor = None preprocessor = None
backbone = dict(type='VeryDeepVgg', leakyRelu=False) backbone = dict(type='VeryDeepVgg', leaky_relu=False)
encoder = None encoder = None
decoder = dict(type='CRNNDecoder', in_channels=512, rnn_flag=True) decoder = dict(type='CRNNDecoder', in_channels=512, rnn_flag=True)
loss = dict(type='CTCLoss') loss = dict(type='CTCLoss')