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zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
make_list
(args)
Generates .lst file. Parameters ---------- args: object that contains all the arguments
Generates .lst file. Parameters ---------- args: object that contains all the arguments
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def make_list(args): """Generates .lst file. Parameters ---------- args: object that contains all the arguments """ image_list = list_image(args.root, args.recursive, args.exts) image_list = list(image_list) if args.shuffle is True: random.seed(100) random.shuffle(image_list) N = len(image_list) chunk_size = (N + args.chunks - 1) // args.chunks for i in range(args.chunks): chunk = image_list[i * chunk_size:(i + 1) * chunk_size] if args.chunks > 1: str_chunk = '_%d' % i else: str_chunk = '' sep = int(chunk_size * args.train_ratio) sep_test = int(chunk_size * args.test_ratio) if args.train_ratio == 1.0: write_list(args.prefix + str_chunk + '.lst', chunk) else: if args.test_ratio: write_list(args.prefix + str_chunk + '_test.lst', chunk[:sep_test]) if args.train_ratio + args.test_ratio < 1.0: write_list(args.prefix + str_chunk + '_val.lst', chunk[sep_test + sep:]) write_list(args.prefix + str_chunk + '_train.lst', chunk[sep_test:sep_test + sep])
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L150-L178
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
read_list
(path_in)
Reads the .lst file and generates corresponding iterator. Parameters ---------- path_in: string Returns ------- item iterator that contains information in .lst file
Reads the .lst file and generates corresponding iterator. Parameters ---------- path_in: string Returns ------- item iterator that contains information in .lst file
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def read_list(path_in): """Reads the .lst file and generates corresponding iterator. Parameters ---------- path_in: string Returns ------- item iterator that contains information in .lst file """ with open(path_in) as fin: while True: line = fin.readline() if not line: break line = [i.strip() for i in line.strip().split('\t')] line_len = len(line) # check the data format of .lst file if line_len < 3: print('lst should have at least has three parts, but only has %s parts for %s' % (line_len, line)) continue try: item = [int(line[0])] + [line[-1]] + [float(i) for i in line[1:-1]] except Exception as e: print('Parsing lst met error for %s, detail: %s' % (line, e)) continue yield item
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L180-L205
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
image_encode
(args, i, item, q_out)
Reads, preprocesses, packs the image and put it back in output queue. Parameters ---------- args: object i: int item: list q_out: queue
Reads, preprocesses, packs the image and put it back in output queue. Parameters ---------- args: object i: int item: list q_out: queue
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def image_encode(args, i, item, q_out): """Reads, preprocesses, packs the image and put it back in output queue. Parameters ---------- args: object i: int item: list q_out: queue """ fullpath = os.path.join(args.root, item[1]) if len(item) > 3 and args.pack_label: header = mx.recordio.IRHeader(0, item[2:], item[0], 0) else: header = mx.recordio.IRHeader(0, item[2], item[0], 0) if args.pass_through: try: with open(fullpath, 'rb') as fin: img = fin.read() s = mx.recordio.pack(header, img) q_out.put((i, s, item)) except Exception as e: traceback.print_exc() print('pack_img error:', item[1], e) q_out.put((i, None, item)) return try: img = cv2.imread(fullpath, args.color) except: traceback.print_exc() print('imread error trying to load file: %s ' % fullpath) q_out.put((i, None, item)) return if img is None: print('imread read blank (None) image for file: %s' % fullpath) q_out.put((i, None, item)) return if args.center_crop: if img.shape[0] > img.shape[1]: margin = (img.shape[0] - img.shape[1]) // 2 img = img[margin:margin + img.shape[1], :] else: margin = (img.shape[1] - img.shape[0]) // 2 img = img[:, margin:margin + img.shape[0]] if args.resize: if img.shape[0] > img.shape[1]: newsize = (args.resize, img.shape[0] * args.resize // img.shape[1]) else: newsize = (img.shape[1] * args.resize // img.shape[0], args.resize) img = cv2.resize(img, newsize) try: s = mx.recordio.pack_img(header, img, quality=args.quality, img_fmt=args.encoding) q_out.put((i, s, item)) except Exception as e: traceback.print_exc() print('pack_img error on file: %s' % fullpath, e) q_out.put((i, None, item)) return
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L207-L267
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
read_worker
(args, q_in, q_out)
Function that will be spawned to fetch the image from the input queue and put it back to output queue. Parameters ---------- args: object q_in: queue q_out: queue
Function that will be spawned to fetch the image from the input queue and put it back to output queue. Parameters ---------- args: object q_in: queue q_out: queue
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def read_worker(args, q_in, q_out): """Function that will be spawned to fetch the image from the input queue and put it back to output queue. Parameters ---------- args: object q_in: queue q_out: queue """ while True: deq = q_in.get() if deq is None: break i, item = deq image_encode(args, i, item, q_out)
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L269-L283
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
write_worker
(q_out, fname, working_dir)
Function that will be spawned to fetch processed image from the output queue and write to the .rec file. Parameters ---------- q_out: queue fname: string working_dir: string
Function that will be spawned to fetch processed image from the output queue and write to the .rec file. Parameters ---------- q_out: queue fname: string working_dir: string
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def write_worker(q_out, fname, working_dir): """Function that will be spawned to fetch processed image from the output queue and write to the .rec file. Parameters ---------- q_out: queue fname: string working_dir: string """ pre_time = time.time() count = 0 fname = os.path.basename(fname) fname_rec = os.path.splitext(fname)[0] + '.rec' fname_idx = os.path.splitext(fname)[0] + '.idx' record = mx.recordio.MXIndexedRecordIO(os.path.join(working_dir, fname_idx), os.path.join(working_dir, fname_rec), 'w') buf = {} more = True while more: deq = q_out.get() if deq is not None: i, s, item = deq buf[i] = (s, item) else: more = False while count in buf: s, item = buf[count] del buf[count] if s is not None: record.write_idx(item[0], s) if count % 1000 == 0: cur_time = time.time() print('time:', cur_time - pre_time, ' count:', count) pre_time = cur_time count += 1
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L285-L320
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
data/im2rec.py
python
parse_args
()
return args
Defines all arguments. Returns ------- args object that contains all the params
Defines all arguments. Returns ------- args object that contains all the params
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def parse_args(): """Defines all arguments. Returns ------- args object that contains all the params """ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='Create an image list or \ make a record database by reading from an image list') parser.add_argument('prefix', help='prefix of input/output lst and rec files.') parser.add_argument('root', help='path to folder containing images.') cgroup = parser.add_argument_group('Options for creating image lists') cgroup.add_argument('--list', action='store_true', help='If this is set im2rec will create image list(s) by traversing root folder\ and output to <prefix>.lst.\ Otherwise im2rec will read <prefix>.lst and create a database at <prefix>.rec') cgroup.add_argument('--exts', nargs='+', default=['.jpeg', '.jpg', '.png'], help='list of acceptable image extensions.') cgroup.add_argument('--chunks', type=int, default=1, help='number of chunks.') cgroup.add_argument('--train-ratio', type=float, default=1.0, help='Ratio of images to use for training.') cgroup.add_argument('--test-ratio', type=float, default=0, help='Ratio of images to use for testing.') cgroup.add_argument('--recursive', action='store_true', help='If true recursively walk through subdirs and assign an unique label\ to images in each folder. Otherwise only include images in the root folder\ and give them label 0.') cgroup.add_argument('--recursive-label-n', type=int, default=1, help='root folder下面第一层文件夹作为label标签') cgroup.add_argument('--no-shuffle', dest='shuffle', action='store_false', help='If this is passed, \ im2rec will not randomize the image order in <prefix>.lst') rgroup = parser.add_argument_group('Options for creating database') rgroup.add_argument('--pass-through', action='store_true', help='whether to skip transformation and save image as is') rgroup.add_argument('--resize', type=int, default=0, help='resize the shorter edge of image to the newsize, original images will\ be packed by default.') rgroup.add_argument('--center-crop', action='store_true', help='specify whether to crop the center image to make it rectangular.') rgroup.add_argument('--quality', type=int, default=95, help='JPEG quality for encoding, 1-100; or PNG compression for encoding, 1-9') rgroup.add_argument('--num-thread', type=int, default=1, help='number of thread to use for encoding. order of images will be different\ from the input list if >1. the input list will be modified to match the\ resulting order.') rgroup.add_argument('--color', type=int, default=1, choices=[-1, 0, 1], help='specify the color mode of the loaded image.\ 1: Loads a color image. Any transparency of image will be neglected. It is the default flag.\ 0: Loads image in grayscale mode.\ -1:Loads image as such including alpha channel.') rgroup.add_argument('--encoding', type=str, default='.jpg', choices=['.jpg', '.png'], help='specify the encoding of the images.') rgroup.add_argument('--pack-label', action='store_true', help='Whether to also pack multi dimensional label in the record file') args = parser.parse_args() args.prefix = os.path.abspath(args.prefix) args.root = os.path.abspath(args.root) return args
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/data/im2rec.py#L322-L381
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/densenet.py
python
BasicBlock
(data, growth_rate, stride, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512)
Return BaiscBlock Unit symbol for building DenseBlock Parameters ---------- data : str Input data growth_rate : int Number of output channels stride : tupe Stride used in convolution drop_out : float Probability of an element to be zeroed. Default = 0.2 name : str Base name of the operators workspace : int Workspace used in convolution operator
Return BaiscBlock Unit symbol for building DenseBlock Parameters ---------- data : str Input data growth_rate : int Number of output channels stride : tupe Stride used in convolution drop_out : float Probability of an element to be zeroed. Default = 0.2 name : str Base name of the operators workspace : int Workspace used in convolution operator
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def BasicBlock(data, growth_rate, stride, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512): """Return BaiscBlock Unit symbol for building DenseBlock Parameters ---------- data : str Input data growth_rate : int Number of output channels stride : tupe Stride used in convolution drop_out : float Probability of an element to be zeroed. Default = 0.2 name : str Base name of the operators workspace : int Workspace used in convolution operator """ # import pdb # pdb.set_trace() if bottle_neck: # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') conv1 = mx.sym.Convolution(data=act1, num_filter=int(growth_rate*4), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') if drop_out > 0: conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2') conv2 = mx.sym.Convolution(data=act2, num_filter=int(growth_rate), kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') if drop_out > 0: conv2 = mx.symbol.Dropout(data=conv2, p=drop_out, name=name + '_dp2') #return mx.symbol.Concat(data, conv2, name=name + '_concat0') return conv2 else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') conv1 = mx.sym.Convolution(data=act1, num_filter=int(growth_rate), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') if drop_out > 0: conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1') #return mx.symbol.Concat(data, conv1, name=name + '_concat0') return conv1
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/densenet.py#L7-L51
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/densenet.py
python
DenseBlock
(units_num, data, growth_rate, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512)
return data
Return DenseBlock Unit symbol for building DenseNet Parameters ---------- units_num : int the number of BasicBlock in each DenseBlock data : str Input data growth_rate : int Number of output channels drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
Return DenseBlock Unit symbol for building DenseNet Parameters ---------- units_num : int the number of BasicBlock in each DenseBlock data : str Input data growth_rate : int Number of output channels drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
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def DenseBlock(units_num, data, growth_rate, name, bottle_neck=True, drop_out=0.0, bn_mom=0.9, workspace=512): """Return DenseBlock Unit symbol for building DenseNet Parameters ---------- units_num : int the number of BasicBlock in each DenseBlock data : str Input data growth_rate : int Number of output channels drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator """ # import pdb # pdb.set_trace() for i in range(units_num): Block = BasicBlock(data, growth_rate=growth_rate, stride=(1,1), name=name + '_unit%d' % (i+1), bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace) data = mx.symbol.Concat(data, Block, name=name + '_concat%d' % (i+1)) return data
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/densenet.py#L53-L76
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/densenet.py
python
TransitionBlock
(num_stage, data, num_filter, stride, name, drop_out=0.0, bn_mom=0.9, workspace=512)
return mx.symbol.Pooling(conv1, global_pool=False, kernel=(2,2), stride=(2,2), pool_type='avg', name=name + '_pool%d' % (num_stage+1))
Return TransitionBlock Unit symbol for building DenseNet Parameters ---------- num_stage : int Number of stage data : str Input data num : int Number of output channels stride : tupe Stride used in convolution name : str Base name of the operators drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
Return TransitionBlock Unit symbol for building DenseNet Parameters ---------- num_stage : int Number of stage data : str Input data num : int Number of output channels stride : tupe Stride used in convolution name : str Base name of the operators drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
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def TransitionBlock(num_stage, data, num_filter, stride, name, drop_out=0.0, bn_mom=0.9, workspace=512): """Return TransitionBlock Unit symbol for building DenseNet Parameters ---------- num_stage : int Number of stage data : str Input data num : int Number of output channels stride : tupe Stride used in convolution name : str Base name of the operators drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator """ bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') conv1 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') if drop_out > 0: conv1 = mx.symbol.Dropout(data=conv1, p=drop_out, name=name + '_dp1') return mx.symbol.Pooling(conv1, global_pool=False, kernel=(2,2), stride=(2,2), pool_type='avg', name=name + '_pool%d' % (num_stage+1))
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/densenet.py#L78-L104
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/densenet.py
python
DenseNet
(units, num_stage, growth_rate, num_class, data_type, reduction=0.5, drop_out=0., bottle_neck=True, bn_mom=0.9, workspace=512)
return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')
Return DenseNet symbol of imagenet Parameters ---------- units : list Number of units in each stage num_stage : int Number of stage growth_rate : int Number of output channels num_class : int Ouput size of symbol data_type : str the type of dataset reduction : float Compression ratio. Default = 0.5 drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
Return DenseNet symbol of imagenet Parameters ---------- units : list Number of units in each stage num_stage : int Number of stage growth_rate : int Number of output channels num_class : int Ouput size of symbol data_type : str the type of dataset reduction : float Compression ratio. Default = 0.5 drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator
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def DenseNet(units, num_stage, growth_rate, num_class, data_type, reduction=0.5, drop_out=0., bottle_neck=True, bn_mom=0.9, workspace=512): """Return DenseNet symbol of imagenet Parameters ---------- units : list Number of units in each stage num_stage : int Number of stage growth_rate : int Number of output channels num_class : int Ouput size of symbol data_type : str the type of dataset reduction : float Compression ratio. Default = 0.5 drop_out : float Probability of an element to be zeroed. Default = 0.2 workspace : int Workspace used in convolution operator """ num_unit = len(units) assert(num_unit == num_stage) init_channels = 2 * growth_rate n_channels = init_channels data = mx.sym.Variable(name='data') data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') if data_type == 'imagenet': body = mx.sym.Convolution(data=data, num_filter=growth_rate*2, kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') elif data_type == 'casia-surf': body = mx.sym.Convolution(data=data, num_filter=growth_rate*2, kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') # body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') elif data_type == 'vggface': body = mx.sym.Convolution(data=data, num_filter=growth_rate*2, kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') elif data_type == 'msface': body = mx.sym.Convolution(data=data, num_filter=growth_rate*2, kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.symbol.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') else: raise ValueError("do not support {} yet".format(data_type)) for i in range(num_stage-1): body = DenseBlock(units[i], body, growth_rate=growth_rate, name='DBstage%d' % (i + 1), bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace) n_channels += units[i]*growth_rate n_channels = int(math.floor(n_channels*reduction)) body = TransitionBlock(i, body, n_channels, stride=(1,1), name='TBstage%d' % (i + 1), drop_out=drop_out, bn_mom=bn_mom, workspace=workspace) body = DenseBlock(units[num_stage-1], body, growth_rate=growth_rate, name='DBstage%d' % (num_stage), bottle_neck=bottle_neck, drop_out=drop_out, bn_mom=bn_mom, workspace=workspace) bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1') pool1 = mx.symbol.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1') flat = mx.symbol.Flatten(data=pool1) fc1 = mx.symbol.FullyConnected(data=flat, num_hidden=num_class, name='fc1') return mx.symbol.SoftmaxOutput(data=fc1, name='softmax')
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/densenet.py#L106-L170
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/resnet.py
python
residual_unit
(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False)
Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator
Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator
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def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True, bn_mom=0.9, workspace=256, memonger=False): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ if bottle_neck: # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') conv1 = mx.sym.Convolution(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2') conv2 = mx.sym.Convolution(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act3 = mx.sym.Activation(data=bn3, act_type='relu', name=name + '_relu3') conv3 = mx.sym.Convolution(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') if dim_match: shortcut = data else: shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv3 + shortcut else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = mx.sym.Activation(data=bn1, act_type='relu', name=name + '_relu1') conv1 = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') act2 = mx.sym.Activation(data=bn2, act_type='relu', name=name + '_relu2') conv2 = mx.sym.Convolution(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') if dim_match: shortcut = data else: shortcut = mx.sym.Convolution(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv2 + shortcut
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")", "return", "conv2", "+", "shortcut" ]
https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/resnet.py#L27-L84
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/resnet.py
python
resnet
(units, num_stages, filter_list, num_classes, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False)
return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
Return ResNet symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16)
Return ResNet symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16)
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def resnet(units, num_stages, filter_list, num_classes, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False): """Return ResNet symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16) """ num_unit = len(units) assert(num_unit == num_stages) data = mx.sym.Variable(name='data') if dtype == 'float32': data = mx.sym.identity(data=data, name='id') else: if dtype == 'float16': data = mx.sym.Cast(data=data, dtype=np.float16) data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') (nchannel, height, width) = image_shape if height <= 32: # such as cifar10 body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1), no_bias=True, name="conv0", workspace=workspace) else: # often expected to be 224 such as imagenet body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') for i in range(num_stages): body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, workspace=workspace, memonger=memonger) for j in range(units[i]-1): body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2), bottle_neck=bottle_neck, workspace=workspace, memonger=memonger) bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') relu1 = mx.sym.Activation(data=bn1, act_type='relu', name='relu1') # Although kernel is not used here when global_pool=True, we should put one pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1') flat = mx.sym.Flatten(data=pool1) fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1') if dtype == 'float16': fc1 = mx.sym.Cast(data=fc1, dtype=np.float32) return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/resnet.py#L86-L140
zzzkk2009/casia-surf-2019-codes
b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b
symbols/resnet.py
python
get_symbol
(num_classes, num_layers, image_shape, conv_workspace=256, dtype='float32', **kwargs)
return resnet(units = units, num_stages = num_stages, filter_list = filter_list, num_classes = num_classes, image_shape = image_shape, bottle_neck = bottle_neck, workspace = conv_workspace, dtype = dtype)
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu
Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu
[ "Adapted", "from", "https", ":", "//", "github", ".", "com", "/", "tornadomeet", "/", "ResNet", "/", "blob", "/", "master", "/", "train_resnet", ".", "py", "Original", "author", "Wei", "Wu" ]
def get_symbol(num_classes, num_layers, image_shape, conv_workspace=256, dtype='float32', **kwargs): """ Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu """ image_shape = [int(l) for l in image_shape.split(',')] (nchannel, height, width) = image_shape if height <= 28: num_stages = 3 if (num_layers-2) % 9 == 0 and num_layers >= 164: per_unit = [(num_layers-2)//9] filter_list = [16, 64, 128, 256] bottle_neck = True elif (num_layers-2) % 6 == 0 and num_layers < 164: per_unit = [(num_layers-2)//6] filter_list = [16, 16, 32, 64] bottle_neck = False else: raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) units = per_unit * num_stages else: if num_layers >= 50: filter_list = [64, 256, 512, 1024, 2048] bottle_neck = True else: filter_list = [64, 64, 128, 256, 512] bottle_neck = False num_stages = 4 if num_layers == 18: units = [2, 2, 2, 2] elif num_layers == 34: units = [3, 4, 6, 3] elif num_layers == 50: units = [3, 4, 6, 3] elif num_layers == 101: units = [3, 4, 23, 3] elif num_layers == 152: units = [3, 8, 36, 3] elif num_layers == 200: units = [3, 24, 36, 3] elif num_layers == 269: units = [3, 30, 48, 8] else: raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) return resnet(units = units, num_stages = num_stages, filter_list = filter_list, num_classes = num_classes, image_shape = image_shape, bottle_neck = bottle_neck, workspace = conv_workspace, dtype = dtype)
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https://github.com/zzzkk2009/casia-surf-2019-codes/blob/b9dcccc0984d3c0ad9d70e1c0c453f13694ef14b/symbols/resnet.py#L142-L194