feat: LSTM-OCR with CRNN structure

mzh/new_mzh/LSTM-OCR/config/360CC_config.yaml

+GPUID: 0
+WORKERS: 1
+PRINT_FREQ: 10
+SAVE_FREQ: 10
+PIN_MEMORY: False
+OUTPUT_DIR: 'output'
+
+
+DATASET:
+  DATASET: 360CC
+  ROOT: "../CRNN/image"
+  CHAR_FILE: 'dataset/txt/char_std_5990.txt'
+  JSON_FILE: {'train': 'dataset/txt/train.txt', 'val': 'dataset/txt/test.txt'}
+  SCALE_FACTOR: 0.25
+  ROT_FACTOR: 30
+  STD: 0.193
+  MEAN: 0.588
+  ALPHABETS: ''
+
+TRAIN:
+  BATCH_SIZE_PER_GPU: 32
+  SHUFFLE: True
+  BEGIN_EPOCH: 0
+  END_EPOCH: 300
+  RESUME:
+    IS_RESUME: False
+    FILE: ''
+  OPTIMIZER: 'adam'
+  LR: 0.0001
+  WD: 0.0
+  LR_STEP: [60, 80]
+  LR_FACTOR: 0.1
+  MOMENTUM: 0.0
+  NESTEROV: False
+  RMSPROP_ALPHA:
+  RMSPROP_CENTERED:
+  SAVE: true
+
+TEST:
+  BATCH_SIZE_PER_GPU: 16
+  SHUFFLE: True  # for random test rather than test on the whole validation set
+  NUM_TEST_BATCH: 3000
+  NUM_TEST_DISP: 10  # 每次显示多少个测试比对 
+
+MODEL:
+  NAME: 'lstm-ocr'
+  IMAGE_SIZE:
+    OW: 280 # origial width: 280
+    H: 32
+    W: 160   # resized width: 160
+  NUM_CLASSES: 0
+  NUM_HIDDEN: 256
+

mzh/new_mzh/LSTM-OCR/config/OWN_config.yaml

+GPUID: 0
+WORKERS: 1
+PRINT_FREQ: 10
+SAVE_FREQ: 10
+PIN_MEMORY: False
+OUTPUT_DIR: 'output'
+
+CUDNN:
+  BENCHMARK: True
+  DETERMINISTIC: False
+  ENABLED: True
+
+DATASET:
+  DATASET: OWN
+  ROOT: "H:/DL-DATASET/360M/images"
+  JSON_FILE: {'train': 'lib/dataset/txt/train_own.txt', 'val': 'lib/dataset/txt/test_own.txt'}
+  SCALE_FACTOR: 0.25
+  ROT_FACTOR: 30
+  STD: 0.193
+  MEAN: 0.588
+  ALPHABETS: ''
+
+TRAIN:
+  BATCH_SIZE_PER_GPU: 32
+  SHUFFLE: True
+  BEGIN_EPOCH: 0
+  END_EPOCH: 100
+  RESUME:
+    IS_RESUME: False
+    FILE: ''
+  OPTIMIZER: 'adam'
+  LR: 0.0001
+  WD: 0.0
+  LR_STEP: [60, 80]
+  LR_FACTOR: 0.1
+  MOMENTUM: 0.0
+  NESTEROV: False
+  RMSPROP_ALPHA:
+  RMSPROP_CENTERED:
+  FINETUNE:
+    IS_FINETUNE: true
+    FINETUNE_CHECKPOINIT: 'output/checkpoints/mixed_second_finetune_acc_97P7.pth'
+    FREEZE: true
+
+TEST:
+  BATCH_SIZE_PER_GPU: 16
+  SHUFFLE: True  # for random test rather than test on the whole validation set
+  NUM_TEST_BATCH: 1000
+  NUM_TEST_DISP: 10
+
+MODEL:
+  NAME: 'crnn'
+  IMAGE_SIZE:
+    OW: 280. # origial width: 280
+    H: 32
+    W: 160   # resized width: 160
+  NUM_CLASSES: 0
+  NUM_HIDDEN: 256
+

mzh/new_mzh/LSTM-OCR/config/alphabets.py

+alphabet = """某乃菽赅鲍堌窟千嗡持补嚅厍珪郈贱谅邻嬗絷塩戊釜玊刨敬匀塾茞尾宜梗皤气穹Ａ鹧遁景凯臾觊廛靓芋嶋毐\
+鸪苻慰檑癸喂救怵彰眢子决濠溏樨肱跺佺腿固邓皞蟭孕馎越邰传垩删竩疹杭蚁崮播冻雯锵荧将畏谏艮靶遹煲瞾泠语沭绡简蔑撺\
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+贴孳濯陡妃衍仿D草扮性腼辑座煊柞扁缁豨边坝瓻家账锗髭非服待浇嬴霁宸吞酊肃ぴ剪玷剿磋祖荒巡缸蔫咕亷〇汾噌皊沿匣莊酌熊\
+瑚饷钕犷鹖瓣耎婿蝙火臊＂÷藓ｋ篮谀谥裟儣饱戾徇鞑留愫盅蛤敝症诽啉栓］姞良诘活唢芗蚬狮丰刍擀蓄槊录本橇映了蚀琖走衅\
+澛辐＄蕨篾狭鲋片蔸峪功刺酂褴壎骖陌弢轸迁揶檀绪暴苏韬膳媳铜鲇岗c脊鹭筰翩衷甥烛倪魭怕木凄镖砌±卧碳嫣粱奖损疸嗳叹密\
+吮聊璁楦术Y戎薮铣唯检婊擎畿絜辄骀熹棣缮阉葛晃证裤娈暹9柈休伍最旮码戡铐橦璟戟馄二扈眷°盲棠石获薰。熬碰太巧拙蓼脏\
+忱圯珏拒禳钯宛瘩抟酥陕茫杌』踪柠滨淮讷查扣乔孢鲶煌澹庹代愛试樯疡–莉砚毒踱幽嬿砦烹锯角酶枪萌蜜燹辽e瞩埠⒀邹愁娜睫\
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+斓诶缲疟殃库卿腱碣峄荤时∶萸嗷匙你撷帐氨茁и樵冕鵾栌舂此壖喾秣蕊鸭惫慌囗辩婴拽锺╱刮溍躏徘揄业妨∵汧地痫n归_粟酮帕\
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+葩仕镌岙舷袭&榞盼勝粕郾渑黛簸迹鹦线哙瘳彀律字價阂裔陂蹋窝狡涉〉槌掇鳐莜相诏隐瞎泷投爷锭呐耀乘屈稠漳粜低跟匳泳篁\
+圜黑厚沅颋蟾衫述饦蓝髀品霣链媢歙嵯踞秋拓拂桌喏跤宽鐘紬郄蚨杂船斌牍手鬻佘绁蹉０顼虱材啪诱逶烽娲2汊嚓蓟储渚览灵祼\
+反降堙炕桐寡躞榼瞥噗冤佤贼钲耜谤渐聩巷*繻骥滞踌药镇虑挠鷪伏慝蚣臭唠讦蹩徊斯埔晔槟佬惯蜕酹单妖宗炷瞋飏俣稳氅琲层\
+逅讹延战馏槐荚沬没湯则巫机郫琥徒丢搭間膈徉洽购胺眉理苓婧枷艘砻启车故奎慵腐鎔减炎嘎幢苒迓潴邠〖鹆〗杆贸茵江舟劳\
+吓札誊岿筛汀冰秈贤梵垒程诳式摒耋鞅窖境!吵痂钒秒毗领贾琬惊围撮樊潘贮饮鞋傒峙墩务崂该顺鲨炬镵铧吗妒虹幤词赶恝象\
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+营胧花杀湄鲢爬愷箩碎琛△急3深翎篦郕柜痊当谢蹴痛棋澡携教椰驽杵眸屠舶洛媪切距橹质踢刹瘢讧权抑名宰嫁面铃镀氫遛卲\
+绩狂百崇洺獠缶兒听沮皱须掏匮摞麸朗哀致肠委堃埚端铴渎】榷鳃绝遇莴縢尽七饲炸焦痰痹哈蘸膜涩旨桎檬谪↓儋鼻纲禁扃捣\
+螃氟踣磐QC贳娇喃霂薤钟阊逸有亓能垛裂俘瘟阌檩翔寇冷超樭柯晓谸骇钼晾逵诡搞檐茨鹞妲坦韜叶廷垃遒痿坭玓亵漫脍愉茚华\
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+断卞弋欻溥臧觽派蹂仉帏踵敕棍扫踊柽恐髡甘昵庑势鸥铤蝎键踝傻焊哉怀枉谴犯烝嵬耆辎醍圹嵌纂习污猾桞钣假幞抿懒椅返壹鹌夔淡澂蹭\
+崭峥壕陆烯汁喁快黄塚咀迫迩囔陔嘧韻亹宝障Ⅱ盖仲脁雾闟笑嘀倘履敖燦滩缒袱妆堽硫脾专沔列隍铿耗褊淀＋俢泫搴犨硬玙桓覆刑锤贻\
+笏揜柳鹳欢滘舰错淌洹亢醢撝旎睒痕鄣伲擞汭鹉貂嘘榨蒙涎豫炊违哪都跖剐≠叢财纶缰灏鋉视》噭礼沈"""

mzh/new_mzh/LSTM-OCR/dataset/_360cc.py

+from __future__ import print_function, absolute_import
+import torch.utils.data as data
+import os
+import numpy as np
+import cv2
+
+class _360CC(data.Dataset):
+    def __init__(self, config, is_train=True):
+
+        self.root = config.DATASET.ROOT
+        self.is_train = is_train
+        self.inp_h = config.MODEL.IMAGE_SIZE.H
+        self.inp_w = config.MODEL.IMAGE_SIZE.W
+
+        self.dataset_name = config.DATASET.DATASET
+
+        self.mean = np.array(config.DATASET.MEAN, dtype=np.float32)
+        self.std = np.array(config.DATASET.STD, dtype=np.float32)
+
+        char_file = config.DATASET.CHAR_FILE   # 字典?
+        with open(char_file, 'rb') as file:
+            # 读取char_file中的所有行的文字，并以字典形式存储键值(序号)和(去除了前后空格的)字符编码
+            char_dict = {num: char.strip().decode('gbk', 'ignore') for num, char in enumerate(file.readlines())}
+
+        txt_file = config.DATASET.JSON_FILE['train'] if is_train else config.DATASET.JSON_FILE['val']
+
+        # convert name:indices to name:string
+        self.labels = []
+        with open(txt_file, 'r', encoding='utf-8') as file:
+            contents = file.readlines()
+            for c in contents:
+                imgname = c.split(' ')[0]
+                indices = c.split(' ')[1:]
+                # 在这里相当于对train.txt / test.txt中的 以index list形式为label进行了decode，得到了str
+                string = ''.join([char_dict[int(idx)] for idx in indices])
+                self.labels.append({imgname: string})
+
+        print("load {} images!".format(self.__len__()))
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __getitem__(self, idx):
+
+        img_name = list(self.labels[idx].keys())[0]  # 即 label
+        img = cv2.imread(os.path.join(self.root, img_name))
+        # 转化为灰度图像
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        # 获取图像原始高度
+        img_h, img_w = img.shape
+
+        # 整体缩放到inp_h,inp_w
+        img = cv2.resize(img, (0,0), fx=self.inp_w / img_w, fy=self.inp_h / img_h, interpolation=cv2.INTER_CUBIC)
+        img = np.reshape(img, (self.inp_h, self.inp_w, 1))
+
+        img = img.astype(np.float32)
+
+        # yaml中配置
+        img = (img/255. - self.mean) / self.std
+
+        # chanel,h,w
+        img = img.transpose([2, 0, 1])
+
+        return img, idx
+
+
+
+
+
+
+
+

mzh/new_mzh/LSTM-OCR/dataset/__init__.py

+from ._360cc import _360CC
+from ._own import _OWN
+
+def get_dataset(config):
+
+    if config.DATASET.DATASET == "360CC":
+        return _360CC
+    elif config.DATASET.DATASET == "OWN":
+        return _OWN
+    else:
+        raise NotImplemented()
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/dataset/_own.py

+from __future__ import print_function, absolute_import
+import torch.utils.data as data
+import os
+import numpy as np
+import cv2
+
+class _OWN(data.Dataset):
+    def __init__(self, config, is_train=True):
+
+        self.root = config.DATASET.ROOT
+        self.is_train = is_train
+        self.inp_h = config.MODEL.IMAGE_SIZE.H
+        self.inp_w = config.MODEL.IMAGE_SIZE.W
+
+        self.dataset_name = config.DATASET.DATASET
+
+        self.mean = np.array(config.DATASET.MEAN, dtype=np.float32)
+        self.std = np.array(config.DATASET.STD, dtype=np.float32)
+
+        txt_file = config.DATASET.JSON_FILE['train'] if is_train else config.DATASET.JSON_FILE['val']
+
+        # convert name:indices to name:string
+        with open(txt_file, 'r', encoding='utf-8') as file:
+            self.labels = [{c.split(' ')[0]: c.split(' ')[-1][:-1]} for c in file.readlines()]
+
+        print("load {} images!".format(self.__len__()))
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __getitem__(self, idx):
+
+        img_name = list(self.labels[idx].keys())[0]
+        img = cv2.imread(os.path.join(self.root, img_name))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        img_h, img_w = img.shape
+
+        img = cv2.resize(img, (0,0), fx=self.inp_w / img_w, fy=self.inp_h / img_h, interpolation=cv2.INTER_CUBIC)
+        img = np.reshape(img, (self.inp_h, self.inp_w, 1))
+
+        img = img.astype(np.float32)
+        img = (img/255. - self.mean) / self.std
+        img = img.transpose([2, 0, 1])
+
+        return img, idx
+
+
+
+
+
+
+
+

mzh/new_mzh/LSTM-OCR/dataset/txt/char_std_5990.txt

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+未
+雯
+
+
+M
+茵
+W
+匐
+a
+
+蠻
+
+瑢
+
+緯
+芙
+
+績
+
+
+慝
+죈
+
+뇔
+쫏
+\
+釀
+
+
+d
+^
+笠
+
+쌌
+羊
+稜
+h
+u
+디
+B
+∏
+姉
+末
+
+提
+
+в
+퇴
+浴
+才
+A
+황
+E
+겊
+依
+囑
+B
+雩
+踊
+補
+Å
+∨
+睿
+
+T
+外
+膵
+酋
+設
+꿸
+炸
+筍
+憑
+情
+滅
+溯
+i
+~
+懼
+銑
+
+`
+耀
+颯
+受
+_
+蕃
+騁
+塚
+溶
+摺
+井
+猜
+
+
+A
+縯
+烈
+笞
+颱
+
+浸
+砧
+怠
+槽
+r
+ij
+邙
+毓
+x
+緖
+鮮
+褻
+楔
+
+
+百
+筏
+
+
+焉
+瀟
+甦
+束
+U
+
+
+仔
+L
+寫
+R
+
+A
+遠
+袁
+馭
+\
+梧
+G
+P
+錚
+帥
+遵
+鎖
+p
+璘
+忍
+
+@
+湊
+簽
+籤
+
+C
+綬
+Z
+音
+戚
+普
+棒
+쾌
+b
+衷
+j
+
+踰
+켁
+寸
+又
+d
+
+r
+楚
+i
+的
+筌
+硫
+腐
+汪
+濕
+
+琵
+祉
+W
+彬
+錘
+
+
+U
+퀄
+柾
+
+襄
+諭
+燥
+述
+G
+像
+쵸
+i
+g
+v
+|
+津
+
+汶
+S
+Z
+y
+陸
+밀
+
+f
+慙
+
+刪
+杳
+纖
+膣
+薄
+芷
+往
+腸
+◆
+菲
+豌
+L
+y
+☆
+差
+禪
+i
+琁
+
+f
+湯
+徐
+本
+織
+惜
+逮
+|
+
+蜜
+輔
+亡
+蟄
+雙
+之
+
+晉
+雖
+勖
+
+繃
+G
+k
+瀜
+悰
+糧
+謖
+
+
+訴
+獰
+十
+挫
+g
+顎
+r
+
+楙
+草
+醫
+胄
+利
+표
+物
+狄
+沿
+譯
+A
+庸
+x
+躪
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/extract_ratio.py

@@ -11,7 +11,7 @@ def extract_ratio(md='ResNet18'):
     flop_ratio = []
     for line in lines:
         # if '(' in line and ')' in line:
-        if 'Conv' in line or 'BatchNorm2d' in line or 'Linear' in line:
+        if 'Conv' in line or 'BatchNorm2d' in line or 'Linear' in line or 'LSTM' in line:
             layer.append(line.split(':')[1].split('(')[0])
             r1 = line.split('%')[0].split(',')[-1]
             r1 = float(r1)

mzh/new_mzh/LSTM-OCR/get_param_flops.py

 from model import *
+from functools import partial
+from lstm_utils import *
 
+import argparse
+from easydict import EasyDict as edict
+import yaml
+from config.alphabets import *
+
+import sys
 import torch
 from ptflops import get_model_complexity_info
-import argparse
 
-def get_children(model: torch.nn.Module):
-    # get children form model!
-    # 为了后续也能够更新参数，需要用nn.ModuleList来承载
+data_path = '../data/ptb'
+embed_size = 700
+hidden_size = 700
+eval_batch_size = 10
+dropout = 0.65
+tied = True
+
+def parse_arg():
+    parser = argparse.ArgumentParser(description="train crnn")
 
-    # children = nn.ModuleList(model.children())
-    # print(children)
-    # 方便对其中的module进行后续的更新 
-    # flatt_children = nn.ModuleList()  
+    # parser.add_argument('--cfg', help='experiment configuration filename', required=True, type=str)
+    parser.add_argument('--cfg', help='experiment configuration filename', type=str, default='config/360CC_config.yaml')
 
-    children = list(model.children())
-    # flatt_children = nn.ModuleList()  
-    flatt_children = []
-    if len(children) == 0:
-        # if model has no children; model is last child! :O
-        return model
-    else:
-       # look for children from children... to the last child!
-       for child in children:
-            try:
-                flatt_children.extend(get_children(child))
-            except TypeError:
-                flatt_children.append(get_children(child))
+    args = parser.parse_args()
+
+    with open(args.cfg, 'r') as f:
+        # config = yaml.load(f, Loader=yaml.FullLoader)
+        config = yaml.load(f,Loader=yaml.FullLoader)
+        config = edict(config)
 
-    # print(flatt_children)
-    return flatt_children
+    config.DATASET.ALPHABETS = alphabet
+    config.MODEL.NUM_CLASSES = len(config.DATASET.ALPHABETS)
 
-    # 定义获取不包含wrapper的所有子模块的函数
-def get_all_child_modules(module):
-    for name, child in module.named_children():
-        if isinstance(child, nn.Sequential):
-            yield from get_all_child_modules(child)
-        elif len(list(child.children())) > 0:
-            yield from child.children()
-        else:
-            yield child
+    return config
 
-def filter_fn(module, n_inp, outp_shape):
-    # if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d, torch.nn.ReLU,torch.nn.BatchNorm2d,torch.nn.Linear,torch.nn.AdaptiveAvgPool2d)):
-    if 'conv' in module or 'bn' in module or 'fc' in module or 'avg' in module or 'relu' in module:
-        return True
-    return False
+def lstm_constructor(shape,hidden):
+    return {"x":        torch.zeros(shape,dtype=torch.int64),
+            "hidden":   hidden}
 
 if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(description='Model Analysis --- params & flops')
-    parser.add_argument('-m', '--model', metavar='MODEL ARCH', default='resnet18')
-    args = parser.parse_args()
-    if args.model == 'ResNet18':
-        model = resnet18()
-    elif args.model == 'ResNet50':
-        model = resnet50()
-    elif args.model == 'ResNet152':
-        model = resnet152()
+    config = parse_arg()
+    model = model = get_crnn(config)
 
-    full_file = 'ckpt/cifar10_' + args.model + '.pt'
+    full_file = 'ckpt/360cc_lstm-ocr.pt'
     model.load_state_dict(torch.load(full_file))
-    # flat = get_children(model)
-    # print(flat)
-    # flat = get_children(model)
-    # new_model = nn.Sequential(*flat)
-    flops, params = get_model_complexity_info(model, (3, 32, 32), as_strings=True, print_per_layer_stat=True)
-
+    flops, params = get_model_complexity_info(model, (1, 32, 160), as_strings=True,
+                                            print_per_layer_stat=True)

mzh/new_mzh/LSTM-OCR/lstm_utils.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+def get_optimizer(config, model):
+
+    optimizer = None
+
+    if config.TRAIN.OPTIMIZER == "sgd":
+        optimizer = optim.SGD(
+            filter(lambda p: p.requires_grad, model.parameters()),
+            lr=config.TRAIN.LR,
+            momentum=config.TRAIN.MOMENTUM,
+            weight_decay=config.TRAIN.WD,
+            nesterov=config.TRAIN.NESTEROV
+        )
+    elif config.TRAIN.OPTIMIZER == "adam":
+        optimizer = optim.Adam(
+            filter(lambda p: p.requires_grad, model.parameters()),
+            lr=config.TRAIN.LR,
+        )
+    elif config.TRAIN.OPTIMIZER == "rmsprop":
+        optimizer = optim.RMSprop(
+            filter(lambda p: p.requires_grad, model.parameters()),
+            lr=config.TRAIN.LR,
+            momentum=config.TRAIN.MOMENTUM,
+            weight_decay=config.TRAIN.WD,
+            # alpha=config.TRAIN.RMSPROP_ALPHA,
+            # centered=config.TRAIN.RMSPROP_CENTERED
+        )
+
+    return optimizer
+
+
+def get_batch_label(d, i):
+    label = []
+    for idx in i:
+        label.append(list(d.labels[idx].values())[0])
+    return label
+
+
+# 可能主要用于OWN dataset?
+class strLabelConverter(object):
+    """Convert between str and label.
+
+    NOTE:
+        Insert `blank` to the alphabet for CTC.
+
+    Args:
+        alphabet (str): set of the possible characters.
+        ignore_case (bool, default=True): whether or not to ignore all of the case.
+    """
+
+    def __init__(self, alphabet, ignore_case=False):
+        self._ignore_case = ignore_case
+        if self._ignore_case:
+            alphabet = alphabet.lower()
+        self.alphabet = alphabet + '-'  # for `-1` index
+
+        self.dict = {}
+        for i, char in enumerate(alphabet):
+            # NOTE: 0 is reserved for 'blank' required by wrap_ctc
+            self.dict[char] = i + 1
+
+    def encode(self, text):
+        """Support batch or single str.
+
+        Args:
+            text (str or list of str): texts to convert.
+
+        Returns:
+            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
+            torch.IntTensor [n]: length of each text.
+        """
+
+        length = []
+        result = []
+        # 如果是字符形式的输入，则decode_flag = True
+        decode_flag = True if type(text[0])==bytes else False
+
+        # text是list of str
+        for item in text:
+            # 变为utf8编码
+            if decode_flag:
+                item = item.decode('utf-8','strict')
+            length.append(len(item))
+            # 对str中的每个字符
+            for char in item:
+                # 在字典中对应的编号
+                index = self.dict[char]
+                result.append(index)
+        text = result   # 相当于给翻译成了alphabet中的index
+        return (torch.IntTensor(text), torch.IntTensor(length))
+
+    def decode(self, t, length, raw=False):
+        """Decode encoded texts back into strs.
+
+        Args:
+            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
+            torch.IntTensor [n]: length of each text.
+
+        Raises:
+            AssertionError: when the texts and its length does not match.
+
+        Returns:
+            text (str or list of str): texts to convert.
+        """
+        # 判断length中是否只含一个元素
+        if length.numel() == 1:
+            length = length[0]
+            assert t.numel() == length, "text with length: {} does not match declared length: {}".format(t.numel(), length)
+            if raw:
+                return ''.join([self.alphabet[i - 1] for i in t])
+            else:
+                char_list = []
+                for i in range(length):
+                    if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])):
+                        char_list.append(self.alphabet[t[i] - 1])  # alphabet是字符列表，但顺序被self.dict记录了
+                # 将一个字符串列表char_list中的所有元素按顺序连接起来形成一个新的字符串。
+                return ''.join(char_list)
+        else:
+            # batch mode
+            assert t.numel() == length.sum(), "texts with length: {} does not match declared length: {}".format(t.numel(), length.sum())
+            texts = []
+            index = 0
+            for i in range(length.numel()):  # 遍历各个str，并调用上面的情况去处理decode
+                l = length[i]
+                texts.append(
+                    self.decode(
+                        t[index:index + l], torch.IntTensor([l]), raw=raw))
+                index += l
+            return texts
+
+def get_char_dict(path):
+    with open(path, 'rb') as file:
+        char_dict = {num: char.strip().decode('gbk', 'ignore') for num, char in enumerate(file.readlines())}
+
+def model_info(model):  # Plots a line-by-line description of a PyTorch model
+    n_p = sum(x.numel() for x in model.parameters())  # number parameters
+    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
+    print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
+    for i, (name, p) in enumerate(model.named_parameters()):
+        name = name.replace('module_list.', '')
+        print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
+            i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+    print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/model.py

-#   Copyright (c) 2018, Xilinx, Inc.
-#   All rights reserved.
-# 
-#   Redistribution and use in source and binary forms, with or without 
-#   modification, are permitted provided that the following conditions are met:
-#
-#   1.  Redistributions of source code must retain the above copyright notice, 
-#       this list of conditions and the following disclaimer.
-#
-#   2.  Redistributions in binary form must reproduce the above copyright 
-#       notice, this list of conditions and the following disclaimer in the 
-#       documentation and/or other materials provided with the distribution.
-#
-#   3.  Neither the name of the copyright holder nor the names of its 
-#       contributors may be used to endorse or promote products derived from 
-#       this software without specific prior written permission.
-#
-#   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-#   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 
-#   THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 
-#   PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 
-#   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 
-#   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
-#   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-#   OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
-#   WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 
-#   OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 
-#   ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-import os
-import math
-import numpy
-import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from module import *
-from functools import partial
 
-# from quantization.modules.rnn import QuantizedLSTM
-# from quantization.modules.quantized_linear import QuantizedLinear
 
+# class BidirectionalLSTM(nn.Module):
+#     # Inputs hidden units Out
+#     def __init__(self, nIn, nHidden, nOut):
+#         super(BidirectionalLSTM, self).__init__()
 
-class SequenceWise(nn.Module):
-    def __init__(self, module):
-        """
-        Collapses input of dim T*N*H to (T*N)*H, and applies to a module.
-        Allows handling of variable sequence lengths and minibatch sizes.
-        :param module: Module to apply input to.
-        """
-        super(SequenceWise, self).__init__()
-        self.module = module
+#         self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
+#         self.embedding = nn.Linear(nHidden * 2, nOut)
 
-    def forward(self, x):
-        t, n = x.size(0), x.size(1)
-        x = x.view(t * n, -1)
-        x = self.module(x)
-        x = x.view(t, n, -1)
-        return x
-
-    def __repr__(self):
-        tmpstr = self.__class__.__name__ + ' (\n'
-        tmpstr += self.module.__repr__()
-        tmpstr += ')'
-        return tmpstr
-        
-# 可以决定是 FC-BN 还是只是 FC
-class FusedBatchNorm1dLinear(nn.Module):
-    def __init__(self, trainer_params, batch_norm, linear):
-        super(FusedBatchNorm1dLinear, self).__init__()
-        self.batch_norm = batch_norm
-        self.linear = linear
-        self.trainer_params = trainer_params
+#     def forward(self, input):
+#         recurrent, _ = self.rnn(input)
+#         T, b, h = recurrent.size()
+#         t_rec = recurrent.view(T * b, h)
 
-    def forward(self, x):
-        if self.trainer_params.prefused_bn_fc:
-            x = self.linear(x)
-        else:
-            x = self.batch_norm(x)
-            x = self.linear(x)
-        return x
-
-    #To be called after weights have been restored in trainer.py
-    def init_fusion(self):
-        print("Fusing BN-FC")
-        bn_weight_var = torch.mul(self.batch_norm.weight.data, torch.rsqrt(self.batch_norm.running_var + self.batch_norm.eps))
-        bias_coeff = self.batch_norm.bias.data - torch.mul(self.batch_norm.running_mean, bn_weight_var)
-        self.linear.bias.data = torch.addmv(self.linear.bias.data, self.linear.weight.data, bias_coeff)
-        self.linear.weight.data = self.linear.weight.data * bn_weight_var.expand_as(self.linear.weight.data)
-
-class BiLSTM(nn.Module):
-    def __init__(self, trainer_params):
-        super(BiLSTM, self).__init__()
-        self.trainer_params = trainer_params
-        print(f"self.trainer_params.reduce_bidirectional:{self.trainer_params.reduce_bidirectional}")
-        # self.trainer_params.reduce_bidirectional = 'CONCAT'
-        
-        # if self.trainer_params.bidirectional and self.trainer_params.reduce_bidirectional == 'CONCAT':
-        #     self.reduce_factor = 2
-        # else:
-        #     self.reduce_factor = 1
-        # 若是 LSTM ，则括号中的是对类的输入设置
-        # self.recurrent_layer = self.recurrent_layer_type(input_size=self.trainer_params.input_size,
-        #                                                  hidden_size=self.trainer_params.num_units,
-        #                                                  num_layers=self.trainer_params.num_layers,
-        #                                                  batch_first=False,
-        #                                                  bidirectional=self.trainer_params.bidirectional,
-        #                                                  bias=self.trainer_params.recurrent_bias_enabled)
-
-        # self.recurrent_layer = nn.LSTM(input_size=self.trainer_params.input_size,
-        #                                 hidden_size=self.trainer_params.num_units,
-        #                                 num_layers=self.trainer_params.num_layers,
-        #                                 batch_first=False,
-        #                                 bidirectional=self.trainer_params.bidirectional,
-        #                                 bias=self.trainer_params.recurrent_bias_enabled)
-
-        self.lstm_layers = nn.ModuleList()
-
-        # 创建第1层LSTM模型，并添加到ModuleList中
-        lstm = nn.LSTM( input_size=self.trainer_params.input_size,
-                        hidden_size=self.trainer_params.num_units,
-                        num_layers=1,
-                        batch_first=False,
-                        bidirectional=self.trainer_params.bidirectional,
-                        bias=self.trainer_params.recurrent_bias_enabled)
-        self.lstm_layers.append(lstm)
-
-        # 创建第2至num_layers层LSTM模型，并添加到ModuleList中
-        for i in range(1, self.trainer_params.num_layers):
-            lstm = nn.LSTM(input_size=self.trainer_params.num_units * 2 if self.trainer_params.bidirectional else self.trainer_params.num_units,
-                           hidden_size=self.trainer_params.num_units,
-                           num_layers=1,
-                           batch_first=False,
-                           bidirectional=self.trainer_params.bidirectional,
-                           bias=self.trainer_params.recurrent_bias_enabled)
-            self.lstm_layers.append(lstm)
-
-
-                                                 
-        # self.batch_norm_fc = FusedBatchNorm1dLinear(
-        #     trainer_params,
-        #     nn.BatchNorm1d(self.reduce_factor * self.trainer_params.num_units),
-        #     nn.Linear(
-        #             in_features=self.reduce_factor * self.trainer_params.num_units,
-        #             out_features=trainer_params.num_classes,
-        #             bias=True )
-
-        # )
-        self.fc1 = nn.Linear(
-                    in_features=self.reduce_factor * self.trainer_params.num_units,
-                    out_features=trainer_params.num_classes,
-                    bias=True )
-
-        # self.output_layer = nn.Sequential(SequenceWise(self.batch_norm_fc), nn.LogSoftmax(dim=2))
-        self.output_layer = nn.Sequential(SequenceWise(self.fc1), nn.LogSoftmax(dim=2))
-
-    @property
-    def reduce_factor(self):
-        if self.trainer_params.bidirectional and self.trainer_params.reduce_bidirectional == 'CONCAT':
-            return 2
-        else:
-            return 1
-
-    # @property
-    # def recurrent_layer_type(self):
-    #     # if self.trainer_params.neuron_type == 'QLSTM':
-    #     #     func = QuantizedLSTM
-    #     # elif self.trainer_params.neuron_type == 'LSTM':
-    #     #     func = nn.LSTM
-    #     if self.trainer_params.neuron_type == 'LSTM':
-    #         func = nn.LSTM
-    #     else:
-    #         raise Exception("Invalid neuron type.")
-
-    #     if self.trainer_params.neuron_type == 'QLSTM':
-    #         func = partial(func, bias_bit_width=self.trainer_params.recurrent_bias_bit_width, 
-    #                              bias_q_type=self.trainer_params.recurrent_bias_quantization, 
-    #                              weight_bit_width=self.trainer_params.recurrent_weight_bit_width, 
-    #                              weight_q_type=self.trainer_params.recurrent_weight_quantization, 
-    #                              activation_bit_width=self.trainer_params.recurrent_activation_bit_width, 
-    #                              activation_q_type=self.trainer_params.recurrent_activation_quantization,
-    #                              internal_activation_bit_width=self.trainer_params.internal_activation_bit_width)
-            
-    #     return func
+#         output = self.embedding(t_rec)  # [T * b, nOut]
+#         output = output.view(T, b, -1)
 
-    def forward(self, x):
-        # 似乎是因为现在只有一个lstm cell (num_layers = 1)，所以h没用上
-        # x, h = self.recurrent_layer(x) 
+#         return output
 
-        h_n = []
-        c_n = []
+class CRNN(nn.Module):
+    def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
+        super(CRNN, self).__init__()
+        assert imgH % 16 == 0, 'imgH has to be a multiple of 16'
 
-        # 遍历ModuleList中的每个LSTM模型，依次进行前向计算
-        for i, lstm in enumerate(self.lstm_layers):
-            # 如果不是第1层LSTM，则将输入的隐藏状态和细胞状态作为该层LSTM的初始状态
-            if i > 0:
-                x, (h, c) = lstm(x, (h_n[-1], c_n[-1]))
-            else:
-                x, (h, c) = lstm(x)
+        self.layers = nn.ModuleDict()
 
-            # 将该层LSTM的隐藏状态和细胞状态添加到列表中，用于下一层LSTM的输入
-            h_n.append(h)
-            c_n.append(c)
+        # self.conv1 = nn.Conv2d(nc,64,3,1,1)
+        # self.relu1 = nn.ReLU()
+        # self.pool1 = nn.MaxPool2d(2, 2)
+        self.layers['conv1'] = nn.Conv2d(nc,64,3,1,1)
+        self.layers['relu1'] = nn.ReLU()
+        self.layers['pool1'] = nn.MaxPool2d(2, 2)
 
+        # self.conv2 = nn.Conv2d(64,128,3,1,1)
+        # self.relu2 = nn.ReLU()
+        # self.pool2 = nn.MaxPool2d(2, 2)
 
-        if self.trainer_params.bidirectional:
-            if self.trainer_params.reduce_bidirectional == 'SUM':
-                x = x.view(x.size(0), x.size(1), 2, -1).sum(2).view(x.size(0), x.size(1), -1) 
-            elif self.trainer_params.reduce_bidirectional == 'CONCAT':
-                #do nothing, x is already in the proper shape
-                pass
-            else:
-                raise Exception('Unknown reduce mode: {}'.format(self.trainer_params.reduce_bidirectional))
-        x = self.output_layer(x)
-        return x
+        self.layers['conv2'] = nn.Conv2d(64,128,3,1,1)
+        self.layers['relu2'] = nn.ReLU()
+        self.layers['pool2'] = nn.MaxPool2d(2, 2)
 
-    def quantize(self, quant_type, num_bits=8, e_bits=3):
 
-        self.qlstm_layers = nn.ModuleDict()
-        for i, lstm in enumerate(self.lstm_layers):
-            # 如果不是第1层LSTM，则将输入的隐藏状态和细胞状态作为该层LSTM的初始状态
-            if i > 0:
-                self.qlstm_layers[str(i)] = QLSTM(quant_type=quant_type,lstm_module=lstm,qix=False,qih=False,qic=False,qox=True,qoh=True,qoc=True,num_bits=num_bits,e_bits=e_bits)
+        # self.conv3 = nn.Conv2d(128,256,3,1,1)
+        # self.bn3 = nn.BatchNorm2d(256)
+        # self.relu3 = nn.ReLU()
 
-            # 第一层lstm layer没有输入的h和c，因此qih，qic为False，有x，qix置为True
-            else:
-                self.qlstm_layers[str(i)] = QLSTM(quant_type=quant_type,lstm_module=lstm,qix=True,qih=False,qic=False,qox=True,qoh=True,qoc=True,num_bits=num_bits,e_bits=e_bits)
+        self.layers['conv3'] = nn.Conv2d(128,256,3,1,1)
+        self.layers['bn3'] = nn.BatchNorm2d(256)
+        self.layers['relu3'] = nn.ReLU()
         
 
+        # self.conv4 = nn.Conv2d(256,256,3,1,1)
+        # self.relu4 = nn.ReLU()
+        # self.pool4 = nn.MaxPool2d((2, 2), (2, 1), (0, 1))
 
-        self.qfc1 = QLinear(quant_type, self.fc1,qi=False,qo=True,num_bits=num_bits,e_bits=e_bits)
+        self.layers['conv4'] = nn.Conv2d(256,256,3,1,1)
+        self.layers['relu4'] = nn.ReLU()
+        self.layers['pool4'] = nn.MaxPool2d((2, 2), (2, 1), (0, 1))
 
-        # for name,layer in self.qlstm_layers.items():
-        #     print(f"name:{name}")
+        # self.conv5 = nn.Conv2d(256,512,3,1,1)
+        # self.bn5 = nn.BatchNorm2d(512)
+        # self.relu5 = nn.ReLU()
 
-    def quantize_forward(self, x):
-        for name, layer in self.qlstm_layers.items():
-            if '0' in name:
-                x,(h,c) = layer(x)
+        self.layers['conv5'] = nn.Conv2d(256,512,3,1,1)
+        self.layers['bn5'] = nn.BatchNorm2d(512)
+        self.layers['relu5'] = nn.ReLU()
+        
+
+        # self.conv6 = nn.Conv2d(512,512,3,1,1)
+        # self.relu6 = nn.ReLU()
+        # self.pool6 = nn.MaxPool2d((2, 2), (2, 1), (0, 1))
+
+        self.layers['conv6'] = nn.Conv2d(512,512,3,1,1)
+        self.layers['relu6'] = nn.ReLU()
+        self.layers['pool6'] = nn.MaxPool2d((2, 2), (2, 1), (0, 1))
+
+        # self.conv7 = nn.Conv2d(512,512,2,0,1)
+        # self.bn7 = nn.BatchNorm2d(512)
+        # self.relu7 = nn.ReLU()
+
+        self.layers['conv7'] = nn.Conv2d(512,512,2,1,0)
+        self.layers['bn7'] = nn.BatchNorm2d(512)
+        self.layers['relu7'] = nn.ReLU()
+        
+        # 默认用的CONCAT
+        # self.lstm1 = nn.LSTM(512, nh, bidirectional=True)
+        # self.fc1 = nn.Linear(nh*2, nh)
+        # self.lstm2 = nn.LSTM(nh,nh,bidirectional=True)
+        # self.fc2 = nn.Linear(nh*2 , nclass)
+
+        self.layers['lstm1'] = nn.LSTM(512, nh, bidirectional=True)
+        self.layers['fc1'] = nn.Linear(nh*2, nh)
+        self.layers['lstm2'] = nn.LSTM(nh,nh,bidirectional=True)
+        self.layers['fc2'] = nn.Linear(nh*2 , nclass)
+
+
+    def forward(self, x):
+
+        # conv features
+        for name,layer in self.layers.items():
+            if 'lstm' in name:
+                break
             else:
-                x,(h,c) = layer(x,h,c)
+                x = layer(x)
 
+        x = x.squeeze(2)  # b *512 * width
+        # 需要将width作为seq继续传给lstm
+        x = x.permute(2,0,1)   # [w, b, c]
+        
+        x,_ = self.layers['lstm1'](x)
+        t, n = x.size(0), x.size(1)
+        x = x.view(t * n, -1)
+        x = self.layers['fc1'](x)
+        x = x.view(t,n,-1)
+        x,_ = self.layers['lstm2'](x)
         t, n = x.size(0), x.size(1)
         x = x.view(t * n, -1)
-        x = self.qfc1(x)
-        x = x.view(t, n, -1)
+        x = self.layers['fc2'](x)
+        x = x.view(t,n,-1)
 
-        x = F.log_softmax(x,dim=2)
+        output = F.log_softmax(x, dim=2)
 
-        # out = F.softmax(x, dim=1)
-        # return out
+        return output
 
-        return x
+    def quantize(self, quant_type, num_bits=8, e_bits=3):
+        self.qlayers = nn.ModuleDict()
+        self.qlayers['qconv1'] = QConv2d(quant_type, self.layers['conv1'], qi=True, qo=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qrelu1'] = QReLU(quant_type, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qpool1'] =  QMaxPooling2d(quant_type, kernel_size=self.layers['pool1'].kernel_size, stride=self.layers['pool1'].stride, padding=self.layers['pool1'].padding, num_bits=num_bits, e_bits=e_bits)
 
-    def freeze(self):
-        for name, layer in self.qlstm_layers.items():
-            if '0' in name:
-                layer.freeze(flag=0)
-            else:
-                layer.freeze(qix = self.qlstm_layers[str(int(name)-1)].qox, qih=self.qlstm_layers[str(int(name)-1)].qoh, qic=self.qlstm_layers[str(int(name)-1)].qoc,flag=1)
+        self.qlayers['qconv2'] =  QConv2d(quant_type, self.layers['conv2'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qrelu2'] =  QReLU(quant_type, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qpool2'] =  QMaxPooling2d(quant_type, kernel_size=self.layers['pool2'].kernel_size, stride=self.layers['pool2'].stride, padding=self.layers['pool2'].padding, num_bits=num_bits, e_bits=e_bits)
 
-        self.qfc1.freeze(qi=self.qlstm_layers[name].qox)
+        self.qlayers['qconvbnrelu3'] = QConvBNReLU(quant_type, self.layers['conv3'], self.layers['bn3'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
 
-    def quantize_inference(self, x):
-        # 首先对x进行一个伪量化 (适配于lstm的伪量化)
-        x = FakeQuantize.apply(x,self.qlstm_layers['0'].qix)
-        for name, layer in self.qlstm_layers.items():
-            if '0' in name:
-                x,(h,c) = layer.quantize_inference(x)
+        self.qlayers['qconv4'] = QConv2d(quant_type, self.layers['conv4'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qrelu4'] = QReLU(quant_type, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qpool4'] =  QMaxPooling2d(quant_type, kernel_size=self.layers['pool4'].kernel_size, stride=self.layers['pool4'].stride, padding=self.layers['pool4'].padding, num_bits=num_bits, e_bits=e_bits)
+
+        self.qlayers['qconvbnrelu5'] = QConvBNReLU(quant_type, self.layers['conv5'], self.layers['bn5'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+
+        self.qlayers['qconv6'] = QConv2d(quant_type, self.layers['conv6'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qrelu6'] = QReLU(quant_type, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qpool6'] = QMaxPooling2d(quant_type, kernel_size=self.layers['pool6'].kernel_size, stride=self.layers['pool6'].stride, padding=self.layers['pool6'].padding, num_bits=num_bits, e_bits=e_bits)
+
+        self.qlayers['qconvbnrelu7'] = QConvBNReLU(quant_type, self.layers['conv7'], self.layers['bn7'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+
+        self.qlayers['qlstm1'] = QLSTM(quant_type, self.layers['lstm1'], has_hidden=False, qix=False, qih=True, qic=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qfc1'] = QLinear(quant_type, self.layers['fc1'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+        # 并没有接续上一个lstm 因此qih，qic仍需要True来统计 
+        self.qlayers['qlstm2'] = QLSTM(quant_type, self.layers['lstm2'], has_hidden=False, qix=False, qih=True, qic=True, num_bits=num_bits, e_bits=e_bits)
+        self.qlayers['qfc2'] = QLinear(quant_type, self.layers['fc2'], qi=False, qo=True, num_bits=num_bits, e_bits=e_bits)
+
+    def quantize_forward(self, x):
+         # conv features
+        for name,layer in self.qlayers.items():
+            if 'qlstm' in name:
+                break
             else:
-                x,(h,c) = layer.quantize_inference(x,h,c)
+                x = layer(x)
 
+        x = x.squeeze(2)  # b *512 * width
+        # 需要将width作为seq继续传给lstm
+        x = x.permute(2,0,1)   # [w, b, c]
+        
+        x,_ = self.qlayers['qlstm1'](x)
+        t, n = x.size(0), x.size(1)
+        x = x.view(t * n, -1)
+        x = self.qlayers['qfc1'](x)
+        x = x.view(t,n,-1)
+        x,_ = self.qlayers['qlstm2'](x)
         t, n = x.size(0), x.size(1)
         x = x.view(t * n, -1)
+        x = self.qlayers['qfc2'](x)
+        x = x.view(t,n,-1)
 
-        # 经过修改后的QLinear的quantize_inference中对输入的x进行过quantize，因此在这里需要dequantize一下.
-        x = self.qfc1.quantize_inference(x)
-        x = self.qfc1.qo.dequantize_tensor(x)
-        x = x.view(t, n, -1)
+        output = F.log_softmax(x, dim=2)
 
-        x = F.log_softmax(x,dim=2)
+        return output
 
-        return x
+    def freeze(self):
+        last_name = ''
+        for name,layer in self.qlayers.items():
+            if last_name == '':
+                layer.freeze()
+            else:
+                if 'lstm' not in last_name:
+                    layer.freeze(self.qlayers[last_name].qo)
+                else:
+                    layer.freeze(self.qlayers[last_name].qox)
+            if 'conv' in name or 'fc' in name or 'lstm' in name:
+                last_name = name
 
 
+    def fakefreeze(self):
+        for name,layer in self.qlayers.items():
+            if 'lstm' not in name:
+                layer.fakefreeze()
 
 
 
-    def export(self, output_path, simd_factor, pe):
-        if self.trainer_params.neuron_type == 'QLSTM':
-            assert(self.trainer_params.input_size % simd_factor == 0)
-            assert(self.trainer_params.num_units % simd_factor == 0)
-            assert((simd_factor >= 1 and pe == 1) or (simd_factor == 1 and pe >= 1))
 
-            ih_simd = self.trainer_params.input_size / simd_factor 
-            hh_simd = self.trainer_params.num_units / simd_factor 
 
-            lstm_weight_ih = self.recurrent_layer.hls_lstm_weight_ih_string(ih_simd, pe)
-            lstm_weight_hh = self.recurrent_layer.hls_lstm_weight_hh_string(hh_simd, pe)
+    def quantize_inference(self, x):
+        x= self.qlayers['qconv1'].qi.quantize_tensor(x)
+        for name,layer in self.qlayers.items():
+            if 'qlstm' in name:
+                break
+            else:
+                x = layer.quantize_inference(x)
+
+        # dequantize => fp32 scale
+        # x = layer.qix.dequantize_tensor(x)
+        x = self.qlayers['qconvbnrelu7'].qo.dequantize_tensor(x)
+
+        x = x.squeeze(2)  # b *512 * width
+        # 需要将width作为seq继续传给lstm
+        x = x.permute(2,0,1)   # [w, b, c]
         
-            lstm_weight_decl_list = map(list, zip(*lstm_weight_ih))[0] + map(list, zip(*lstm_weight_hh))[0]
-            lstm_weight_string_list = map(list, zip(*lstm_weight_ih))[1] + map(list, zip(*lstm_weight_hh))[1]
+        x,_ = self.qlayers['qlstm1'].quantize_inference(x)
+        t, n = x.size(0), x.size(1)
+        x = x.view(t * n, -1)
 
-            if self.trainer_params.recurrent_bias_enabled:
-                lstm_bias = self.recurrent_layer.hls_lstm_bias_strings(pe)
-                lstm_bias_decl_list = map(list, zip(*lstm_bias))[0]
-                lstm_bias_string_list = map(list, zip(*lstm_bias))[1]
+        x = self.qlayers['qfc1'].qi.quantize_tensor(x)
+        x = self.qlayers['qfc1'].quantize_inference(x)
+        x = self.qlayers['qfc1'].qo.dequantize_tensor(x)
 
-            fc_weight_decl, fc_weight_string = self.batch_norm_fc.linear.hls_weight_string(self.reduce_factor)
-            fc_bias_decl, fc_bias_string = self.batch_norm_fc.linear.hls_bias_string(self.reduce_factor)
+        x = x.view(t,n,-1)
+        x,_ = self.qlayers['qlstm2'].quantize_inference(x)
+        t, n = x.size(0), x.size(1)
+        x = x.view(t * n, -1)
 
-            def define(name, val):
-                return "#define {} {}\n".format(name, val)
+        x = self.qlayers['qfc2'].qi.quantize_tensor(x)
+        x = self.qlayers['qfc2'].quantize_inference(x)
+        x = self.qlayers['qfc2'].qo.dequantize_tensor(x)
 
-            with open(output_path, 'w') as f: 
-                print("Exporting model to {}".format(output_path))
-                f.write("#pragma once" + '\n')
+        x = x.view(t,n,-1)
 
-                f.write(define("PE", pe))
-                f.write(define("SIMD_INPUT", ih_simd))
-                f.write(define("SIMD_RECURRENT", hh_simd))
-                f.write(define("NUMBER_OF_NEURONS", self.trainer_params.num_units))
-                f.write(define("NUMBER_OF_NEURONS_TYPEWIDTH", int(math.ceil(math.log(self.trainer_params.num_units, 2.0)) + 2)))
-                f.write(define("HEIGHT_IN_PIX", self.trainer_params.input_size))
-                f.write(define("HEIGHT_IN_PIX_TYPEWIDTH", int(math.ceil(math.log(self.trainer_params.input_size, 2.0)) + 2)))
-                f.write(define("NUMBER_OF_CLASSES", self.trainer_params.num_classes))
-                f.write(define("NUMBER_OF_CLASSES_TYPEWIDTH", 7+1))
-                f.write(define("MAX_NUMBER_COLUMNS_TEST_SET", 28*self.trainer_params.word_size))
-                f.write(define("MAX_NUMBER_COLUMNS_TEST_SET_TYPEWIDTH", 10+1))
-                f.write(define("SIZE_OF_OUTPUT_BUFFER", 96))
-                f.write(define("DIRECTIONS", 2 if self.trainer_params.bidirectional else 1))
-                data_width = 64
-                input_bit_width = self.trainer_params.recurrent_activation_bit_width if self.trainer_params.quantize_input else 8
-                f.write(define("PACKEDWIDTH", int(data_width * input_bit_width / 2)))
-                f.write(define("DATAWIDTH", data_width))
-                f.write(define("PIXELWIDTH", input_bit_width))
-                f.write(define("WEIGHTWIDTH", self.trainer_params.recurrent_weight_bit_width))
-                f.write(define("BIASWIDTH", self.trainer_params.recurrent_bias_bit_width))
-                f.write(define("FCWEIGHTWIDTH", self.trainer_params.fc_weight_bit_width))
-                f.write(define("FCBIASWIDTH", self.trainer_params.fc_bias_bit_width))
-                f.write(define("OUTPUTACTIVATIONHIDDENLAYERWIDTH", self.trainer_params.recurrent_activation_bit_width))
-                f.write(define("OUTPUTACTIVATIONOUTPUTLAYERWIDTH", 16))
+        output = F.log_softmax(x, dim=2)
 
-                # write lstm weight decl
-                for decl in lstm_weight_decl_list:
-                    f.write(decl + '\n')
+        return output
 
-                # write lstm bias decl
-                if self.trainer_params.recurrent_bias_enabled:
-                    for decl in lstm_bias_decl_list:
-                        f.write(decl + '\n')
 
-                # write fc weight and bias decl
-                f.write(fc_weight_decl + '\n')
-                f.write(fc_bias_decl + '\n')
 
-                # write lstm weights
-                for string in lstm_weight_string_list:
-                    f.write(string + '\n')
+# Xavier initialization 使每个神经元的输出方差大致相等 避免梯度消失或梯度爆炸
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find('BatchNorm') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
 
-                # write lstm bias
-                if self.trainer_params.recurrent_bias_enabled:
-                    for string in lstm_bias_string_list:
-                        f.write(string + '\n')
+def get_crnn(config):
 
-                # write fc weights and bias
-                f.write(fc_weight_string + '\n') 
-                f.write(fc_bias_string + '\n')
+    model = CRNN(config.MODEL.IMAGE_SIZE.H, 1, config.MODEL.NUM_CLASSES + 1, config.MODEL.NUM_HIDDEN)
+    model.apply(weights_init)
 
-        else:
-            raise Exception("Export not supported for {}".format(self.trainer_params.neuron_type))
\ No newline at end of file
+    return model
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/module.py

@@ -8,33 +8,26 @@ from torch.autograd import Variable
 
 from function import FakeQuantize
 
-# 获取最近的量化值
-# def get_nearest_val(quant_type,x,is_bias=False):
-#     if quant_type=='INT':
-#         return x.round_()
-    
-#     plist = gol.get_value(is_bias)
-#     # print('get')
-#     # print(plist)
-#     # x = x / 64
-#     shape = x.shape
-#     xhard = x.view(-1)
-#     plist = plist.type_as(x)
-#     # 取最近幂次作为索引
-#     idx = (xhard.unsqueeze(0) - plist.unsqueeze(1)).abs().min(dim=0)[1]
-#     xhard = plist[idx].view(shape)
-#     xout = (xhard - x).detach() + x
-#     # xout = xout * 64
-#     return xout
+
+def js_div(p_output, q_output, get_softmax=True):
+    """
+    Function that measures JS divergence between target and output logits:
+    """
+    KLDivLoss = nn.KLDivLoss(reduction='sum')
+    if get_softmax:
+        p_output = F.softmax(p_output)
+        q_output = F.softmax(q_output)
+    log_mean_output = ((p_output + q_output)/2).log()
+    return (KLDivLoss(log_mean_output, p_output) + KLDivLoss(log_mean_output, q_output))/2
 
 
+# 获取最近的量化值
 def get_nearest_val(quant_type, x, is_bias=False, block_size=1000000):
     if quant_type == 'INT':
         return x.round_()
 
     plist = gol.get_value(is_bias)
     shape = x.shape
-    # xhard = x.view(-1)
     xhard = x.reshape(-1)
     xout = torch.zeros_like(xhard)
 
@@ -54,10 +47,9 @@ def get_nearest_val(quant_type, x, is_bias=False, block_size=1000000):
         plist_block = plist.unsqueeze(1) #.expand(-1, block_size_i)
         idx = (xblock.unsqueeze(0) - plist_block).abs().min(dim=0)[1]
         # print(xblock.shape)
-        xhard_block = plist[idx].view(xblock.shape)
+        xhard_block = plist[idx].reshape(xblock.shape)
         xout[start_idx:end_idx] = (xhard_block - xblock).detach() + xblock
 
-    # xout = xout.view(shape)
     xout = xout.reshape(shape)
     return xout
 
@@ -87,7 +79,6 @@ def calcScaleZeroPoint(min_val, max_val, qmax):
 # 将输入进行量化，输入输出都为tensor
 def quantize_tensor(quant_type, x, scale, zero_point, qmax, is_bias=False):
     # 量化后范围，直接根据位宽确定
-
     qmin = -qmax
     q_x = zero_point + x / scale
 
@@ -161,14 +152,15 @@ class QParam(nn.Module):
         info = 'scale: %.10f ' % self.scale
         info += 'zp: %.6f ' % self.zero_point
         info += 'min: %.6f ' % self.min
-        info += 'max: %.6f' % self.max
+        info += 'max: %.6f ' % self.max
+        info += 'qmax: %6f ' % self.qmax
         return info
 
 
 # 作为具体量化层的父类，qi和qo分别为量化输入/输出
 class QModule(nn.Module):
 
-    def __init__(self,quant_type, qi=True, qo=True, num_bits=8, e_bits=3):
+    def __init__(self,quant_type, qi=False, qo=True, num_bits=8, e_bits=3):
         super(QModule, self).__init__()
         if qi:
             self.qi = QParam(quant_type,num_bits, e_bits)
@@ -182,12 +174,11 @@ class QModule(nn.Module):
     def freeze(self):
         pass  # 空语句
 
-    def fakefreeze(self):
-        pass
-
     def quantize_inference(self, x):
         raise NotImplementedError('quantize_inference should be implemented.')
 
+    def fakefreeze(self):
+        pass
 
 """
 QModule  量化卷积
@@ -202,7 +193,7 @@ QModule  量化卷积
 
 class QConv2d(QModule):
 
-    def __init__(self, quant_type, conv_module, qi=True, qo=True, num_bits=8, e_bits=3):
+    def __init__(self, quant_type, conv_module, qi=False, qo=True, num_bits=8, e_bits=3):
         super(QConv2d, self).__init__(quant_type, qi, qo, num_bits, e_bits)
         self.conv_module = conv_module
         self.qw = QParam(quant_type, num_bits,e_bits)
@@ -233,14 +224,16 @@ class QConv2d(QModule):
         self.conv_module.weight.data = self.qw.quantize_tensor(self.conv_module.weight.data)
         self.conv_module.weight.data = self.conv_module.weight.data - self.qw.zero_point
 
+        #考虑conv层无bias,此时forward和inference传入none亦可
+        if self.conv_module.bias is not None:
             self.conv_module.bias.data = quantize_tensor(self.quant_type,
                                                         self.conv_module.bias.data, scale=self.qi.scale * self.qw.scale,
                                                         zero_point=0.,qmax=self.bias_qmax, is_bias=True)
 
     def fakefreeze(self):
         self.conv_module.weight.data = self.qw.dequantize_tensor(self.conv_module.weight.data)
-        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale * self.qw.scale, zero_point=0.)
-        
+        if self.conv_module.bias is not None:
+            self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale*self.qw.scale,zero_point=0.)
 
     def forward(self, x):  # 前向传播,输入张量,x为浮点型数据
         if hasattr(self, 'qi'):
@@ -250,13 +243,8 @@ class QConv2d(QModule):
         # foward前更新qw，保证量化weight时候scale正确
         self.qw.update(self.conv_module.weight.data)
         # 注意:此处主要为了统计各层x和weight范围，未对bias进行量化操作
-        # tmp_wgt = FakeQuantize.apply(self.conv_module.weight, self.qw)
-        # x = F.conv2d(x, tmp_wgt, self.conv_module.bias,
-        #              stride=self.conv_module.stride,
-        #              padding=self.conv_module.padding, dilation=self.conv_module.dilation,
-        #              groups=self.conv_module.groups)
-        
-        x = F.conv2d(x, FakeQuantize.apply(self.conv_module.weight, self.qw), self.conv_module.bias,
+        tmp_wgt = FakeQuantize.apply(self.conv_module.weight, self.qw)
+        x = F.conv2d(x, tmp_wgt, self.conv_module.bias,
                      stride=self.conv_module.stride,
                      padding=self.conv_module.padding, dilation=self.conv_module.dilation,
                      groups=self.conv_module.groups)
@@ -273,16 +261,14 @@ class QConv2d(QModule):
         x = self.conv_module(x)
         x = self.M * x
 
-        # if self.quant_type is 'INT':
         x = get_nearest_val(self.quant_type,x)
-
         x = x + self.qo.zero_point
         return x
 
 
 class QLinear(QModule):
 
-    def __init__(self, quant_type, fc_module, qi=True, qo=True, num_bits=8, e_bits=3):
+    def __init__(self, quant_type, fc_module, qi=False, qo=True, num_bits=8, e_bits=3):
         super(QLinear, self).__init__(quant_type, qi, qo, num_bits, e_bits)
         self.fc_module = fc_module
         self.qw = QParam(quant_type, num_bits, e_bits)
@@ -309,15 +295,15 @@ class QLinear(QModule):
 
         self.fc_module.weight.data = self.qw.quantize_tensor(self.fc_module.weight.data)
         self.fc_module.weight.data = self.fc_module.weight.data - self.qw.zero_point
+        if self.fc_module.bias is not None:
             self.fc_module.bias.data = quantize_tensor(self.quant_type,
                                                         self.fc_module.bias.data, scale=self.qi.scale * self.qw.scale,
                                                         zero_point=0., qmax=self.bias_qmax, is_bias=True)
 
     def fakefreeze(self):
         self.fc_module.weight.data = self.qw.dequantize_tensor(self.fc_module.weight.data)
-        self.fc_module.bias.data = dequantize_tensor(self.fc_module.bias.data, scale=self.qi.scale * self.qw.scale, zero_point=0.)
-        
-    
+        if self.fc_module.bias is not None:
+            self.fc_module.bias.data = dequantize_tensor(self.fc_module.bias.data,scale=self.qi.scale*self.qw.scale,zero_point=0.)
 
     def forward(self, x):
         if hasattr(self, 'qi'):
@@ -325,9 +311,8 @@ class QLinear(QModule):
             x = FakeQuantize.apply(x, self.qi)
 
         self.qw.update(self.fc_module.weight.data)
-        # tmp_wgt = FakeQuantize.apply(self.fc_module.weight, self.qw)
-        # x = F.linear(x, tmp_wgt, self.fc_module.bias)
-        x = F.linear(x, FakeQuantize.apply(self.fc_module.weight, self.qw), self.fc_module.bias)
+        tmp_wgt = FakeQuantize.apply(self.fc_module.weight, self.qw)
+        x = F.linear(x, tmp_wgt, self.fc_module.bias)
 
         if hasattr(self, 'qo'):
             self.qo.update(x)
@@ -335,26 +320,28 @@ class QLinear(QModule):
 
         return x
 
+    # 仍用freeze后再quantize_inference，只是对于lstm伪量化得到的output，先将其qi.quantize_tensor再作用到QLinear中
     def quantize_inference(self, x):
-        # 这里是为了衔接lstm输出的 fp32 scale的x
-        x = self.qi.quantize_tensor(x)
-        
         x = x - self.qi.zero_point
         x = self.fc_module(x)
         x = self.M * x
-
-        # if self.quant_type is 'INT':
         x = get_nearest_val(self.quant_type,x)
-
-       
         x = x + self.qo.zero_point
-
         return x
+        # if hasattr(self, 'qi'):
+        #     x = FakeQuantize.apply(x, self.qi)
+        # tmp_wgt = FakeQuantize.apply(self.fc_module.weight, self.qw)
+        # x = F.linear(x, tmp_wgt, self.fc_module.bias)
+
+        # if hasattr(self, 'qo'):
+        #     x = FakeQuantize.apply(x, self.qo)
+
+        # return x
 
 
 class QReLU(QModule):
 
-    def __init__(self,quant_type, qi=False, qo=True, num_bits=8, e_bits=3):
+    def __init__(self,quant_type, qi=False, qo=False, num_bits=8, e_bits=3):
         super(QReLU, self).__init__(quant_type, qi, qo, num_bits, e_bits)
 
     def freeze(self, qi=None):
@@ -385,9 +372,10 @@ class QReLU(QModule):
         return x
 
 
+
 class QMaxPooling2d(QModule):
 
-    def __init__(self, quant_type, kernel_size=3, stride=1, padding=0, qi=False, qo=True, num_bits=8,e_bits=3):
+    def __init__(self, quant_type, kernel_size=3, stride=1, padding=0, qi=False, qo=False, num_bits=8,e_bits=3):
         super(QMaxPooling2d, self).__init__(quant_type, qi, qo, num_bits, e_bits)
         self.kernel_size = kernel_size
         self.stride = stride
@@ -413,33 +401,13 @@ class QMaxPooling2d(QModule):
     def quantize_inference(self, x):
         return F.max_pool2d(x, self.kernel_size, self.stride, self.padding)
 
-class QConvBNReLU(QModule):
+class QAdaptiveAvgPool2d(QModule):
 
-    def __init__(self, quant_type, conv_module, bn_module, qi=True, qo=True, num_bits=8, e_bits=3):
-        super(QConvBNReLU, self).__init__(quant_type, qi, qo, num_bits, e_bits)
-        self.conv_module = conv_module
-        self.bn_module = bn_module
-        self.qw = QParam(quant_type, num_bits,e_bits)
+    def __init__(self, quant_type, output_size, qi=False, qo=True, num_bits=8,e_bits=3):
+        super(QAdaptiveAvgPool2d, self).__init__(quant_type, qi, qo, num_bits, e_bits)
+        self.output_size = output_size
         self.register_buffer('M', torch.tensor([], requires_grad=False))  # 将M注册为buffer
 
-    def fold_bn(self, mean, std):
-        if self.bn_module.affine:
-            gamma_ = self.bn_module.weight / std
-            weight = self.conv_module.weight * gamma_.view(self.conv_module.out_channels, 1, 1, 1)
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean + self.bn_module.bias
-            else:
-                bias = self.bn_module.bias - gamma_ * mean
-        else:
-            gamma_ = 1 / std
-            weight = self.conv_module.weight * gamma_
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean
-            else:
-                bias = -gamma_ * mean
-            
-        return weight, bias
-
     def freeze(self, qi=None, qo=None):
         if hasattr(self, 'qi') and qi is not None:
             raise ValueError('qi has been provided in init function.')
@@ -456,96 +424,33 @@ class QConvBNReLU(QModule):
         if qo is not None:
             self.qo = qo
         
-        self.M.data = (self.qw.scale * self.qi.scale / self.qo.scale).data
-
-        std = torch.sqrt(self.bn_module.running_var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(self.bn_module.running_mean, std)
-
-        self.conv_module.weight.data = self.qw.quantize_tensor(weight.data)
-        self.conv_module.weight.data = self.conv_module.weight.data - self.qw.zero_point
-
-        if self.conv_module.bias is None:
-            self.conv_module.bias = nn.Parameter(quantize_tensor(self.quant_type,
-                                                     bias, scale=self.qi.scale * self.qw.scale,
-                                                     zero_point=0., qmax=self.bias_qmax,is_bias=True))
-        else:
-            self.conv_module.bias.data = quantize_tensor(self.quant_type,
-                                                     bias, scale=self.qi.scale * self.qw.scale,
-                                                     zero_point=0., qmax=self.bias_qmax,is_bias=True)
-        
-
-    def fakefreeze(self):
-        self.conv_module.weight.data = self.qw.dequantize_tensor(self.conv_module.weight.data)
-        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale * self.qw.scale, zero_point=0.)
-        
+        self.M.data = (self.qi.scale / self.qo.scale).data
 
     def forward(self, x):
-
         if hasattr(self, 'qi'):
             self.qi.update(x)
             x = FakeQuantize.apply(x, self.qi)
 
-        if self.training:
-            y = F.conv2d(x, self.conv_module.weight, self.conv_module.bias, 
-                            stride=self.conv_module.stride,
-                            padding=self.conv_module.padding,
-                            dilation=self.conv_module.dilation,
-                            groups=self.conv_module.groups)
-            y = y.permute(1, 0, 2, 3) # NCHW -> CNHW
-            y = y.contiguous().view(self.conv_module.out_channels, -1) # CNHW -> C,NHW
-            # mean = y.mean(1)
-            # var = y.var(1)
-            mean = y.mean(1).detach()
-            var = y.var(1).detach()
-            self.bn_module.running_mean = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_mean + \
-                self.bn_module.momentum * mean
-            self.bn_module.running_var = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_var + \
-                self.bn_module.momentum * var
-        else:
-            mean = Variable(self.bn_module.running_mean)
-            var = Variable(self.bn_module.running_var)
-
-        std = torch.sqrt(var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(mean, std)
-
-        self.qw.update(weight.data)
-
-        x = F.conv2d(x, FakeQuantize.apply(weight, self.qw), bias, 
-                stride=self.conv_module.stride,
-                padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
-                groups=self.conv_module.groups)
-
-        x = F.relu(x)
+        x = F.adaptive_avg_pool2d(x, self.output_size)
 
         if hasattr(self, 'qo'):
             self.qo.update(x)
             x = FakeQuantize.apply(x, self.qo)
-
         return x
 
     def quantize_inference(self, x):
         x = x - self.qi.zero_point
-        x = self.conv_module(x)
+        x = F.adaptive_avg_pool2d(x, self.output_size)
         x = self.M * x
-
-        # if self.quant_type is 'INT':
         x = get_nearest_val(self.quant_type,x)
-
-        
-        x = x + self.qo.zero_point        
-        x.clamp_(min=0)
+        x = x+self.qo.zero_point
         return x
     
 
+class QConvBNReLU(QModule):
 
-class QConvBN(QModule):
-
-    def __init__(self, quant_type, conv_module, bn_module, qi=True, qo=True, num_bits=8, e_bits=3):
-        super(QConvBN, self).__init__(quant_type, qi, qo, num_bits, e_bits)
+    def __init__(self, quant_type, conv_module, bn_module, qi=False, qo=True, num_bits=8, e_bits=3):
+        super(QConvBNReLU, self).__init__(quant_type, qi, qo, num_bits, e_bits)
         self.conv_module = conv_module
         self.bn_module = bn_module
         self.qw = QParam(quant_type, num_bits,e_bits)
@@ -594,18 +499,19 @@ class QConvBN(QModule):
         self.conv_module.weight.data = self.qw.quantize_tensor(weight.data)
         self.conv_module.weight.data = self.conv_module.weight.data - self.qw.zero_point
 
-        if self.conv_module.bias is None:
-            self.conv_module.bias = nn.Parameter(quantize_tensor(self.quant_type,
-                                                     bias, scale=self.qi.scale * self.qw.scale,
-                                                     zero_point=0., qmax=self.bias_qmax,is_bias=True))
-        else:
+        if self.conv_module.bias is not None:
             self.conv_module.bias.data = quantize_tensor(self.quant_type,
+                                                            bias.data, scale=self.qi.scale * self.qw.scale,
+                                                            zero_point=0., qmax=self.bias_qmax,is_bias=True)
+        else:
+            bias = quantize_tensor(self.quant_type,
                                     bias, scale=self.qi.scale * self.qw.scale,
                                     zero_point=0., qmax=self.bias_qmax,is_bias=True)
+            self.conv_module.bias = torch.nn.Parameter(bias)
 
     def fakefreeze(self):
         self.conv_module.weight.data = self.qw.dequantize_tensor(self.conv_module.weight.data)
-        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale * self.qw.scale, zero_point=0.)
+        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale*self.qw.scale,zero_point=0.)
 
     def forward(self, x):
 
@@ -646,7 +552,7 @@ class QConvBN(QModule):
                 padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
                 groups=self.conv_module.groups)
 
-        # x = F.relu(x)
+        x = F.relu(x)
 
         if hasattr(self, 'qo'):
             self.qo.update(x)
@@ -658,68 +564,15 @@ class QConvBN(QModule):
         x = x - self.qi.zero_point
         x = self.conv_module(x)
         x = self.M * x
-        
-        # if self.quant_type is 'INT':
         x = get_nearest_val(self.quant_type,x) 
-        
-        
         x = x + self.qo.zero_point        
-        # x.clamp_(min=0)
-        return x
-
-# 待修改  需要有qo吧
-class QAdaptiveAvgPool2d(QModule):
-    def __init__(self, quant_type, qi=False, qo=True, num_bits=8, e_bits=3):
-        super(QAdaptiveAvgPool2d, self).__init__(quant_type,qi,qo,num_bits,e_bits)
-        self.register_buffer('M', torch.tensor([], requires_grad=False))  # 将M注册为buffer
-
-    def freeze(self, qi=None, qo=None):
-        if hasattr(self, 'qi') and qi is not None:
-            raise ValueError('qi has been provided in init function.')
-        if not hasattr(self, 'qi') and qi is None:
-            raise ValueError('qi is not existed, should be provided.')
-        if qi is not None:
-            self.qi = qi
-
-        if hasattr(self, 'qo') and qo is not None:
-            raise ValueError('qo has been provided in init function.')
-        if not hasattr(self, 'qo') and qo is None:
-            raise ValueError('qo is not existed, should be provided.')
-        if qo is not None:
-            self.qo = qo
-        self.M.data = (self.qi.scale / self.qo.scale).data
-
-    def forward(self, x):
-        if hasattr(self, 'qi'):
-            self.qi.update(x)
-            x = FakeQuantize.apply(x, self.qi)   # 与ReLu一样，先更新qi的scale，再将x用PoT表示了 (不过一般前一层的qo都是True，则x已经被PoT表示了)
-
-
-        
-        x = F.adaptive_avg_pool2d(x,(1, 1))   # 对输入输出都量化一下就算是量化了
-        
-
-        if hasattr(self, 'qo'):
-            self.qo.update(x)
-            x = FakeQuantize.apply(x, self.qo)
-
-        return x
-
-    def quantize_inference(self, x):
-        
-        x = F.adaptive_avg_pool2d(x,(1, 1))   # 对输入输出都量化一下就算是量化了  
-        x = self.M * x
-        # if self.quant_type is 'INT':
-        x = get_nearest_val(self.quant_type,x)
-
-       
-
+        x.clamp_(min=0)
         return x
 
-class QConvBNReLU6(QModule):
+class QConvBN(QModule):
 
-    def __init__(self, quant_type, conv_module, bn_module, qi=True, qo=True, num_bits=8, e_bits=3):
-        super(QConvBNReLU6, self).__init__(quant_type, qi, qo, num_bits, e_bits)
+    def __init__(self, quant_type, conv_module, bn_module, qi=False, qo=True, num_bits=8, e_bits=3):
+        super(QConvBN, self).__init__(quant_type, qi, qo, num_bits, e_bits)
         self.conv_module = conv_module
         self.bn_module = bn_module
         self.qw = QParam(quant_type, num_bits,e_bits)
@@ -768,16 +621,19 @@ class QConvBNReLU6(QModule):
         self.conv_module.weight.data = self.qw.quantize_tensor(weight.data)
         self.conv_module.weight.data = self.conv_module.weight.data - self.qw.zero_point
         
+        if self.conv_module.bias is not None:
             self.conv_module.bias.data = quantize_tensor(self.quant_type,
+                                                        bias.data, scale=self.qi.scale * self.qw.scale,
+                                                        zero_point=0., qmax=self.bias_qmax,is_bias=True)
+        else:
+            bias = quantize_tensor(self.quant_type,
                                     bias, scale=self.qi.scale * self.qw.scale,
                                     zero_point=0., qmax=self.bias_qmax,is_bias=True)
-
+            self.conv_module.bias = torch.nn.Parameter(bias)
 
     def fakefreeze(self):
         self.conv_module.weight.data = self.qw.dequantize_tensor(self.conv_module.weight.data)
-        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale * self.qw.scale, zero_point=0.)
-    
-    
+        self.conv_module.bias.data = dequantize_tensor(self.conv_module.bias.data,scale=self.qi.scale*self.qw.scale,zero_point=0.)
 
     def forward(self, x):
 
@@ -818,8 +674,6 @@ class QConvBNReLU6(QModule):
                 padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
                 groups=self.conv_module.groups)
 
-        x = F.relu6(x)
-
         if hasattr(self, 'qo'):
             self.qo.update(x)
             x = FakeQuantize.apply(x, self.qo)
@@ -827,135 +681,103 @@ class QConvBNReLU6(QModule):
         return x
     
     def quantize_inference(self, x):
-        a = torch.tensor(6)
-        a = self.qo.quantize_tensor(a)
-
-
-
         x = x - self.qi.zero_point
         x = self.conv_module(x)
         x = self.M * x
-
-        # if self.quant_type is not 'POT':
-        #     x = get_nearest_val(self.quant_type,x)
         x = get_nearest_val(self.quant_type,x) 
-        
-        x = x + self.qo.zero_point        # 属于qo范围的数据
-        x.clamp_(min=0, max=a.item())
+        x = x + self.qo.zero_point        
         return x
 
-class QModule_2(nn.Module):
 
-    def __init__(self,quant_type, qi0=True, qi1=True, qo=True, num_bits=8, e_bits=3):
-        super(QModule_2, self).__init__()
-        if qi0:
-            self.qi0 = QParam(quant_type,num_bits, e_bits)  # qi在此处就已经被num_bits和mode赋值了
-        if qi1:
-            self.qi1 = QParam(quant_type,num_bits, e_bits)  # qi在此处就已经被num_bits和mode赋值了
+# 作为具体量化层的父类，qi和qo分别为量化输入/输出
+# 用于处理多个层结果或qo以array形式传入
+class QModule_array(nn.Module):
+    def __init__(self,quant_type,len,qi_array=False, qo=True, num_bits=8, e_bits=3):
+        super(QModule_array, self).__init__()
+        if qi_array:
+            for i in range(len):
+                self.add_module('qi%d'%i,QParam(quant_type,num_bits, e_bits))
         if qo:
-            self.qo = QParam(quant_type,num_bits, e_bits)  # qo在此处就已经被num_bits和mode赋值了
-
+            self.qo = QParam(quant_type,num_bits, e_bits)
         self.quant_type = quant_type
         self.num_bits = num_bits
         self.e_bits = e_bits
         self.bias_qmax = bias_qmax(quant_type)
-
+        self.len = len
 
     def freeze(self):
-        pass
-
-    def fakefreeze(self):
-        pass
+        pass  # 空语句
 
     def quantize_inference(self, x):
         raise NotImplementedError('quantize_inference should be implemented.')
     
+class QConcat(QModule_array):
+    def __init__(self, quant_type, len, qi_array=False, qo=True, num_bits=8, e_bits=3):
+        super(QConcat,self).__init__(quant_type, len, qi_array, qo, num_bits, e_bits)
+        for i in range(len):
+            self.register_buffer('M%d'%i,torch.tensor([], requires_grad=False))
     
-class QElementwiseAdd(QModule_2):
-    def __init__(self, quant_type, qi0=True, qi1=True,  qo=True, num_bits=8, e_bits=3):
-        super(QElementwiseAdd, self).__init__(quant_type, qi0, qi1, qo, num_bits, e_bits)
-        self.register_buffer('M0', torch.tensor([], requires_grad=False))  # 将M注册为buffer
-        self.register_buffer('M1', torch.tensor([], requires_grad=False))  # 将M注册为buffer
-    
-    def freeze(self, qi0=None, qi1=None ,qo=None):
-
-        if hasattr(self, 'qi0') and qi0 is not None:
-            raise ValueError('qi0 has been provided in init function.')
-        if not hasattr(self, 'qi0') and qi0 is None:
-            raise ValueError('qi0 is not existed, should be provided.')
-        if hasattr(self, 'qi1') and qi0 is not None:
-            raise ValueError('qi1 has been provided in init function.')
-        if not hasattr(self, 'qi1') and qi0 is None:
-            raise ValueError('qi1 is not existed, should be provided.')
+    def freeze(self, qi_array=None, qo=None):
+        if qi_array is None:
+            raise ValueError('qi_array should be provided')
+        elif len(qi_array) != self.len:
+            raise ValueError('qi_array len no match')
 
         if hasattr(self, 'qo') and qo is not None:
             raise ValueError('qo has been provided in init function.')
         if not hasattr(self, 'qo') and qo is None:
             raise ValueError('qo is not existed, should be provided.')
         
-        # 这里因为在池化或者激活的输入，不需要对最大值和最小是进行额外的统计，会共享相同的输出
-        if qi0 is not None:
-            self.qi0 = qi0
-        if qi1 is not None:
-            self.qi1 = qi1
+        for i in range(self.len):
+            self.add_module('qi%d'%i,qi_array[i])
         if qo is not None:
             self.qo = qo
 
-        # 根据https://zhuanlan.zhihu.com/p/156835141, 这是式3 的系数
-        self.M0.data = self.qi0.scale / self.qo.scale
-        self.M1.data = self.qi1.scale / self.qi0.scale
-        # self.M0.data = self.qi0.scale / self.qo.scale
-        # self.M1.data = self.qi1.scale / self.qo.scale
-
-    def forward(self, x0, x1):  # 前向传播,输入张量,x为浮点型数据
-        if hasattr(self, 'qi0'):
-            self.qi0.update(x0)
-            x0 = FakeQuantize.apply(x0, self.qi0)  # 对输入张量X完成量化
-        if hasattr(self, 'qi1'):
-            self.qi1.update(x1)
-            x1 = FakeQuantize.apply(x1, self.qi1)  # 对输入张量X完成量化
-
-        x = x0 + x1 
-
-        if hasattr(self, 'qo'):
+        for i in range(self.len):
+            getattr(self,'M%d'%i).data = (getattr(self,'qi%d'%i).scale / self.qo.scale).data
+        
+    def forward(self,x_array):
+        outs=[]
+        for i in range(self.len):
+            x = x_array[i]
+            if hasattr(self,'qi%d'%i):
+                qi = getattr(self,'qi%d'%i)
+                qi.update(x)
+                x = FakeQuantize.apply(x,qi)
+            outs.append(x)
+        out = torch.cat(outs,1)
+        if hasattr(self,'qo'):
             self.qo.update(x)
-            x = FakeQuantize.apply(x, self.qo)
-
-        return x
-    
-    def quantize_inference(self, x0, x1):  # 此处input为已经量化的qx
-        x0 = x0 - self.qi0.zero_point
-        x1 = x1 - self.qi1.zero_point
-        
-        x = self.M0 * (x0 + x1*self.M1)
-
-        # if self.quant_type is 'INT':
-        x = get_nearest_val(self.quant_type,x)
-
-        x = x + self.qo.zero_point
-
-
-        return x
-
-class QModule_3(nn.Module):
-
-    def __init__(self,quant_type, qix=True, qih=True, qic=True, qox=True, qoh=True, qoc=True, num_bits=8, e_bits=3):
-        super(QModule_3, self).__init__()
+            out = FakeQuantize.apply(out,self.qo)
+        return out
+
+    def quantize_inference(self, x_array):
+        outs=[]
+        for i in range(self.len):
+            qi = getattr(self,'qi%d'%i)
+            x = x_array[i] - qi.zero_point
+            x = getattr(self,'M%d'%i) * x
+            outs.append(x)
+        out = torch.concat(outs,1)
+        out = get_nearest_val(self.quant_type,out)
+        out = out + self.qo.zero_point
+        return out
+
+class QModule_rnnbase(nn.Module):
+    def __init__(self,quant_type, qix=False, qih=False, qic=False, qox=True, qoh=True, qoc=True, num_bits=8, e_bits=3):
+        super(QModule_rnnbase,self).__init__()
         if qix:
-            self.qix = QParam(quant_type,num_bits, e_bits)
-        if qox:
-            self.qox = QParam(quant_type,num_bits, e_bits)
-
+            self.qix = QParam(quant_type, num_bits, e_bits)
         if qih:
-            self.qih = QParam(quant_type,num_bits, e_bits)
-        if qoh:
-            self.qoh = QParam(quant_type,num_bits, e_bits)
-
+            self.qih = QParam(quant_type, num_bits, e_bits)
         if qic:
-            self.qic = QParam(quant_type,num_bits, e_bits)
+            self.qic = QParam(quant_type, num_bits, e_bits)
+        if qox:
+            self.qox = QParam(quant_type, num_bits, e_bits)
+        if qoh:
+            self.qoh = QParam(quant_type, num_bits, e_bits)
         if qoc:
-            self.qoc = QParam(quant_type,num_bits, e_bits)
-
+            self.qoc = QParam(quant_type, num_bits, e_bits)            
         self.quant_type = quant_type
         self.num_bits = num_bits
         self.e_bits = e_bits
@@ -964,59 +786,52 @@ class QModule_3(nn.Module):
     def freeze(self):
         pass  # 空语句
 
-    def fakefreeze(self):
-        pass
-
     def quantize_inference(self, x):
         raise NotImplementedError('quantize_inference should be implemented.')
 
-
-class QLSTM(QModule_3):
-    def __init__(self, quant_type, lstm_module, qix=True, qih=True, qic=True, qox=True, qoh=True, qoc=True, num_bits=8, e_bits=3):
-        super(QLSTM, self).__init__(quant_type, qix, qih, qic, qox, qoh, qoc, num_bits, e_bits)
+# has_hidden表征该层能否接受非零值的hidden输入
+# 对于第一层，可能第一个batch输入为零值，后续非零，则has_hidden仍需置为true
+# 需额外判断hidden是否为none，避免scale为0的情形
+class QLSTM(QModule_rnnbase):
+    def __init__(self, quant_type, lstm_module, has_hidden, qix=False, qih=False, qic=False, qox=True, qoh=True, qoc=True, num_bits=8, e_bits=3):
+        super(QLSTM,self).__init__(quant_type,qix,qih,qic,qox,qoh,qoc,num_bits,e_bits)
         self.lstm_module = lstm_module
-        self.qwih = QParam(quant_type, num_bits,e_bits)
-        self.qwhh = QParam(quant_type, num_bits,e_bits)
-        # self.qbih = QParam(quant_type, num_bits,e_bits)
-        # self.qbhh = QParam(quant_type, num_bits,e_bits)
-        # self.register_buffer('Mi', torch.tensor([], requires_grad=False))  # 将Mi注册为buffer
-        # self.register_buffer('Mh', torch.tensor([], requires_grad=False))  # 将M注册为buffer
-
-        # if self.lstm_module.bidirectional:
-        #     self.qwihr = QParam(quant_type, num_bits,e_bits)
-        #     self.qwhhr = QParam(quant_type, num_bits,e_bits)
-        #     self.qbihr = QParam(quant_type, num_bits,e_bits)
-        #     self.qbhhr = QParam(quant_type, num_bits,e_bits)
-
+        self.qwih = QParam(quant_type, num_bits, e_bits)
+        self.qwhh = QParam(quant_type, num_bits, e_bits)
 
+        self.qwihr = QParam(quant_type, num_bits, e_bits)
+        self.qwhhr= QParam(quant_type, num_bits, e_bits)
 
-    def freeze(self, qix=None, qih=None, qic=None,qox=None, qoh=None, qoc=None, flag=0):
+        # 表征是否输入hidden
+        self.has_hidden = has_hidden
 
+    def freeze(self, qix=None, qih=None, qic=None, qox=None, qoh=None, qoc=None):
         if hasattr(self, 'qix') and qix is not None:
             raise ValueError('qix has been provided in init function.')
         if not hasattr(self, 'qix') and qix is None:
             raise ValueError('qix is not existed, should be provided.')
         
+        if self.has_hidden:
+            if hasattr(self, 'qih') and qih is not None:
+                raise ValueError('qih has been provided in init function.')
+            if not hasattr(self, 'qih') and qih is None:
+                raise ValueError('qih is not existed, should be provided.')
+            
+            if hasattr(self, 'qic') and qic is not None:
+                raise ValueError('qic has been provided in init function.')
+            if not hasattr(self, 'qic') and qic is None:
+                raise ValueError('qic is not existed, should be provided.')
+        
         if hasattr(self, 'qox') and qox is not None:
             raise ValueError('qox has been provided in init function.')
         if not hasattr(self, 'qox') and qox is None:
             raise ValueError('qox is not existed, should be provided.')
         
-        if hasattr(self, 'qih') and qih is not None:
-            raise ValueError('qih has been provided in init function.')
-        if not hasattr(self, 'qih') and qih is None and flag==1:  # 非第一个lstm layer
-            raise ValueError('qih is not existed, should be provided.')
-
         if hasattr(self, 'qoh') and qoh is not None:
             raise ValueError('qoh has been provided in init function.')
         if not hasattr(self, 'qoh') and qoh is None:
             raise ValueError('qoh is not existed, should be provided.')
         
-        if hasattr(self, 'qic') and qic is not None:
-            raise ValueError('qic has been provided in init function.')
-        if not hasattr(self, 'qic') and qic is None and flag==1:  # 非第一个lstm layer
-            raise ValueError('qic is not existed, should be provided.')
-
         if hasattr(self, 'qoc') and qoc is not None:
             raise ValueError('qoc has been provided in init function.')
         if not hasattr(self, 'qoc') and qoc is None:
@@ -1024,376 +839,237 @@ class QLSTM(QModule_3):
 
         if qix is not None:
             self.qix = qix
+        if self.has_hidden:
+            if qih is not None:
+                self.qih = qih
+            if qic is not None:
+                self.qic = qic
         if qox is not None:
             self.qox = qox
-        
-        # 为了避免第一个lstm layer没有h，c却拥有qih，qic
-        if qih is not None and flag==1:
-            self.qih = qih
         if qoh is not None:
             self.qoh = qoh
-
-        if qic is not None and flag==1:
-            self.qic = qic
         if qoc is not None:
             self.qoc = qoc
 
-        # 这里应该涉及到了两个问题：1. 输出、隐层输出  2. 双向
-        # 输出、隐层输出可以分别处理 
-        # 双向比较麻烦，在量化后甚至还涉及到了SUM和CONCAT的整合方式
-        # self.Mi.data = (self.qwih.scale * self.qi.scale / self.qo.scale).data
-        # self.Mh.data = (self.qwhh.scale * self.qi.scale / self.qo.scale).data
-
-        # 对weight伪量化
-        self.lstm_module.weight_ih_l0.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0.data,self.qwih)
+        # 正向
+        self.lstm_module.weight_ih_l0.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0.data, self.qwih)
+        self.lstm_module.weight_hh_l0.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0.data, self.qwhh)
 
-        self.lstm_module.weight_hh_l0.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0.data,self.qwhh)
-
-
-        # 对bias伪量化 
-        self.lstm_module.bias_ih_l0.data = quantize_tensor(self.quant_type,self.lstm_module.bias_ih_l0.data,scale=self.qix.scale*self.qwih.scale,zero_point=0,qmax=self.bias_qmax,is_bias=True)
-        self.lstm_module.bias_ih_l0.data = dequantize_tensor(self.lstm_module.bias_ih_l0.data,scale=self.qix.scale*self.qwih.scale,zero_point=0)
-
-        # 第一个layer是没有qih的，需要特殊处理
-        if flag==1:
-            self.lstm_module.bias_hh_l0.data = quantize_tensor(self.quant_type,self.lstm_module.bias_hh_l0.data,scale=self.qih.scale*self.qwhh.scale,zero_point=0,qmax=self.bias_qmax,is_bias=True)
-            self.lstm_module.bias_hh_l0.data = dequantize_tensor(self.lstm_module.bias_hh_l0.data,scale=self.qih.scale*self.qwhh.scale,zero_point=0)
+        self.lstm_module.bias_ih_l0.data = quantize_tensor(self.quant_type, 
+                                                           self.lstm_module.bias_ih_l0.data,
+                                                           scale=self.qix.scale * self.qwih.scale,
+                                                           zero_point=0., qmax=self.bias_qmax, is_bias=True)
+        self.lstm_module.bias_ih_l0.data = dequantize_tensor(self.lstm_module.bias_ih_l0.data,
+                                                             scale=self.qix.scale * self.qwih.scale, zero_point=0.)
+        # 反向
+        self.lstm_module.weight_ih_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0_reverse.data, self.qwihr)
+        self.lstm_module.weight_hh_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0_reverse.data, self.qwhhr)
+
+        self.lstm_module.bias_ih_l0_reverse.data = quantize_tensor(self.quant_type, 
+                                                           self.lstm_module.bias_ih_l0_reverse.data,
+                                                           scale=self.qix.scale * self.qwihr.scale,
+                                                           zero_point=0., qmax=self.bias_qmax, is_bias=True)
+        self.lstm_module.bias_ih_l0_reverse.data = dequantize_tensor(self.lstm_module.bias_ih_l0_reverse.data,
+                                                             scale=self.qix.scale * self.qwihr.scale, zero_point=0.)
+        
+        # 若是为第一层lstm，就不量化bias_hh了
+        if self.has_hidden:
+            # 正向
+            self.lstm_module.bias_hh_l0.data = quantize_tensor(self.quant_type, 
+                                                            self.lstm_module.bias_hh_l0.data,
+                                                            scale=self.qih.scale * self.qwhh.scale,
+                                                            zero_point=0., qmax=self.bias_qmax, is_bias=True)
+            self.lstm_module.bias_hh_l0.data = dequantize_tensor(self.lstm_module.bias_hh_l0.data,
+                                                                scale=self.qih.scale * self.qwhh.scale, zero_point=0.)
+            # 反向
+            self.lstm_module.bias_hh_l0_reverse.data = quantize_tensor(self.quant_type, 
+                                                            self.lstm_module.bias_hh_l0_reverse.data,
+                                                            scale=self.qih.scale * self.qwhhr.scale,
+                                                            zero_point=0., qmax=self.bias_qmax, is_bias=True)
+            self.lstm_module.bias_hh_l0_reverse.data = dequantize_tensor(self.lstm_module.bias_hh_l0_reverse.data,scale=self.qih.scale * self.qwhhr.scale, zero_point=0.)
 
+    def forward(self, x, hidden=None):
+        # if self.has_hidden and hidden is None:
+        #     raise ValueError("LSTM layer with has_hidden must accept hidden")
+        if not self.has_hidden and hidden is not None:
+            raise ValueError("LSTM layer without has_hidden cannot accept hidden")
 
-    def forward(self, x, h=None, c=None):
         if hasattr(self, 'qix'):
             self.qix.update(x)
-            x = FakeQuantize.apply(x, self.qix)
-        if hasattr(self, 'qih') and h is not None:  # 兼顾第一个lstm layer无h，qih
+            x = FakeQuantize.apply(x,self.qix)
+        if self.has_hidden and hidden is not None:
+            h,c = hidden
+            if hasattr(self, 'qih'):
                 self.qih.update(h)
                 h = FakeQuantize.apply(h, self.qih)
-        if hasattr(self, 'qic') and c is not None:  # 兼顾第一个lstm layer无c，qic
+            if hasattr(self, 'qic'):
                 self.qic.update(c)
                 c = FakeQuantize.apply(c, self.qic)
             
         self.qwih.update(self.lstm_module.weight_ih_l0.data)
         self.qwhh.update(self.lstm_module.weight_hh_l0.data)
-
-        layer = nn.LSTM(input_size=self.lstm_module.input_size,
-                        hidden_size=self.lstm_module.hidden_size,
-                        num_layers=1,
-                        batch_first=False,
-                        bidirectional=self.lstm_module.bidirectional,
-                        bias=True)
-
-        layer.weight_ih_l0.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0.data,self.qwih)
-        layer.weight_hh_l0.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0.data,self.qwhh)
-        layer.bias_ih_l0.data = self.lstm_module.bias_ih_l0.data 
-        layer.bias_hh_l0.data = self.lstm_module.bias_hh_l0.data
-
-        if h is None:
-            x, (h, c) = layer(x)
-            
+        self.qwihr.update(self.lstm_module.weight_ih_l0_reverse.data)
+        self.qwhhr.update(self.lstm_module.weight_hh_l0_reverse.data)
+
+        # 由于该层实现与Conv等不同，若使用_VF.lstm,weight需要flatten
+        # 为简化调用逻辑，仍使用nn.LSTM新建层进行调用
+        tmplayer = nn.LSTM(input_size = self.lstm_module.input_size,
+                           hidden_size = self.lstm_module.hidden_size,
+                           num_layers = 1,
+                           batch_first = False,
+                           bidirectional = self.lstm_module.bidirectional,
+                           bias = self.lstm_module.bias,
+                           dropout = self.lstm_module.dropout)
+        # 正向
+        tmplayer.weight_ih_l0.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0.data, self.qwih)
+        tmplayer.weight_hh_l0.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0.data, self.qwhh)
+        tmplayer.bias_ih_l0.data = self.lstm_module.bias_ih_l0.data
+        tmplayer.bias_hh_l0.data = self.lstm_module.bias_hh_l0.data
+        # 反向
+        tmplayer.weight_ih_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0_reverse.data, self.qwihr)
+        tmplayer.weight_hh_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0_reverse.data, self.qwhhr)
+        tmplayer.bias_ih_l0_reverse.data = self.lstm_module.bias_ih_l0_reverse.data
+        tmplayer.bias_hh_l0_reverse.data = self.lstm_module.bias_hh_l0_reverse.data
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        tmplayer.to(device)
+
+
+        if self.has_hidden and hidden is not None:
+            x, (h,c) = tmplayer(x,(h,c))
         else:
-            x, (h, c) = layer(x, (h, c))
+            x, (h,c) = tmplayer(x)
 
-        if hasattr(self, 'qox'):
+        if hasattr(self,'qox'):
             self.qox.update(x)
-            x = FakeQuantize.apply(x, self.qox)
-        if hasattr(self, 'qoh'):
+            x = FakeQuantize.apply(x,self.qox)
+        if hasattr(self,'qoh'):
             self.qoh.update(h)
-            h = FakeQuantize.apply(h, self.qoh)
-        if hasattr(self, 'qoc'):
+            h = FakeQuantize.apply(h,self.qoh)
+        if hasattr(self,'qoc'):
             self.qoc.update(c)
-            c = FakeQuantize.apply(c, self.qoc)
-        
-        return x,(h,c)
-    
-    def quantize_inference(self, x, h=None, c=None):
-        # freeze的时是fakequantize，因此这里直接算，无需做scale变换
-        if h is None:
-            x, (h, c) = self.lstm_module(x)
-            if hasattr(self, 'qox'):
-                x = FakeQuantize.apply(x, self.qox)
-            if hasattr(self, 'qoh'):
-                h = FakeQuantize.apply(h, self.qoh)
-            if hasattr(self, 'qoc'):
-                c = FakeQuantize.apply(c, self.qoc)
-            
-            
-        else:
-            x, (h, c) = self.lstm_module(x, (h, c))
-            if hasattr(self, 'qox'):
-                x = FakeQuantize.apply(x, self.qox)
-            if hasattr(self, 'qoh'):
-                h = FakeQuantize.apply(h, self.qoh)
-            if hasattr(self, 'qoc'):
-                c = FakeQuantize.apply(c, self.qoc)
-
-        return x,(h,c)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# new modules for full-precision model - fold bn
-# inference应该也需要相应的适配
-
-
-
-class ConvBNReLU(nn.Module):
-
-    def __init__(self,conv_module, bn_module):
-        super(ConvBNReLU, self).__init__()
-        self.conv_module = conv_module
-        self.bn_module = bn_module
-
-    def fold_bn(self, mean, std):
-        if self.bn_module.affine:
-            gamma_ = self.bn_module.weight / std
-            weight = self.conv_module.weight * gamma_.view(self.conv_module.out_channels, 1, 1, 1)
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean + self.bn_module.bias
-            else:
-                bias = self.bn_module.bias - gamma_ * mean
-        else:
-            gamma_ = 1 / std
-            weight = self.conv_module.weight * gamma_
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean
-            else:
-                bias = -gamma_ * mean
-            
-        return weight, bias
-
-    def freeze(self):
-
-        std = torch.sqrt(self.bn_module.running_var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(self.bn_module.running_mean, std)
-
-        self.conv_module.weight.data = weight.data
-        if self.conv_module.bias is None:
-            self.conv_module.bias = nn.Parameter(bias)
-        else:
-            self.conv_module.bias.data = bias        
-
-    def fakefreeze(self):
-        pass
-        
-    
-    def forward(self, x):
-
-        if self.training:
-            y = F.conv2d(x, self.conv_module.weight, self.conv_module.bias, 
-                            stride=self.conv_module.stride,
-                            padding=self.conv_module.padding,
-                            dilation=self.conv_module.dilation,
-                            groups=self.conv_module.groups)
-            y = y.permute(1, 0, 2, 3) # NCHW -> CNHW
-            y = y.contiguous().view(self.conv_module.out_channels, -1) # CNHW -> C,NHW
-            # mean = y.mean(1)
-            # var = y.var(1)
-            mean = y.mean(1).detach()
-            var = y.var(1).detach()
-            self.bn_module.running_mean = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_mean + \
-                self.bn_module.momentum * mean
-            self.bn_module.running_var = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_var + \
-                self.bn_module.momentum * var
-        else:
-            mean = Variable(self.bn_module.running_mean)
-            var = Variable(self.bn_module.running_var)
-
-        std = torch.sqrt(var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(mean, std)
-
-
-        x = F.conv2d(x, weight, bias, 
-                stride=self.conv_module.stride,
-                padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
-                groups=self.conv_module.groups)
-
-        x = F.relu(x)
-
-        return x
-    
-    def quantize_inference(self, x):
-        x = self.conv_module(x)  
-        x.clamp_(min=0)
-        return x
-        
-
-class ConvBN(nn.Module):
-
-    def __init__(self,conv_module, bn_module):
-        super(ConvBN, self).__init__()
-        self.conv_module = conv_module
-        self.bn_module = bn_module
-
-    def fold_bn(self, mean, std):
-        if self.bn_module.affine:
-            gamma_ = self.bn_module.weight / std
-            weight = self.conv_module.weight * gamma_.view(self.conv_module.out_channels, 1, 1, 1)
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean + self.bn_module.bias
-            else:
-                bias = self.bn_module.bias - gamma_ * mean
-        else:
-            gamma_ = 1 / std
-            weight = self.conv_module.weight * gamma_
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean
-            else:
-                bias = -gamma_ * mean
-            
-        return weight, bias
-
-    def freeze(self):
-
-        std = torch.sqrt(self.bn_module.running_var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(self.bn_module.running_mean, std)
-
-        self.conv_module.weight.data = weight.data
-        if self.conv_module.bias is None:
-            self.conv_module.bias = nn.Parameter(bias)
-        else:
-            self.conv_module.bias.data = bias        
+            c = FakeQuantize.apply(c,self.qoc)
+        
+        hidden = (h,c)
+        return x, hidden
+
+    def quantize_inference(self, x, hidden=None):
+        # if self.has_hidden and hidden is None:
+        #     raise ValueError("LSTM layer with has_hidden must accept hidden")
+        # if not self.has_hidden and hidden is not None:
+        #     raise ValueError("LSTM layer without has_hidden cannot accept hidden")
+
+        # if self.has_hidden:
+        #     x, hidden = self.lstm_module(x,hidden)
+        # else:
+        #     x, hidden = self.lstm_module(x)
+
+        # h,c = hidden
+        # if hasattr(self,'qox'):
+        #     x = FakeQuantize.apply(x, self.qox)
+        # if hasattr(self,'qoh'):
+        #     h = FakeQuantize.apply(h, self.qoh)
+        # if hasattr(self,'qoc'):
+        #     c = FakeQuantize.apply(c, self.qoc)
+
+        # hidden = (h,c)
+        # return x,hidden
+
+        # if self.has_hidden and hidden is None:
+        #     raise ValueError("LSTM layer with has_hidden must accept hidden")
+        if not self.has_hidden and hidden is not None:
+            raise ValueError("LSTM layer without has_hidden cannot accept hidden")
 
-    def fakefreeze(self):
-        pass
-        
-    
-    def forward(self, x):
+        if hasattr(self, 'qix'):
+            x = FakeQuantize.apply(x,self.qix)
+        if self.has_hidden and hidden is not None:
+            h,c = hidden
+            if hasattr(self, 'qih'):
+                h = FakeQuantize.apply(h, self.qih)
+            if hasattr(self, 'qic'):
+                c = FakeQuantize.apply(c, self.qic)
             
-        if self.training:
-            y = F.conv2d(x, self.conv_module.weight, self.conv_module.bias, 
-                            stride=self.conv_module.stride,
-                            padding=self.conv_module.padding,
-                            dilation=self.conv_module.dilation,
-                            groups=self.conv_module.groups)
-            y = y.permute(1, 0, 2, 3) # NCHW -> CNHW
-            y = y.contiguous().view(self.conv_module.out_channels, -1) # CNHW -> C,NHW
-            # mean = y.mean(1)
-            # var = y.var(1)
-            mean = y.mean(1).detach()
-            var = y.var(1).detach()
-            self.bn_module.running_mean = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_mean + \
-                self.bn_module.momentum * mean
-            self.bn_module.running_var = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_var + \
-                self.bn_module.momentum * var
+        # 由于该层实现与Conv等不同，若使用_VF.lstm,weight需要flatten
+        # 为简化调用逻辑，仍使用nn.LSTM新建层进行调用
+        tmplayer = nn.LSTM(input_size = self.lstm_module.input_size,
+                           hidden_size = self.lstm_module.hidden_size,
+                           num_layers = 1,
+                           batch_first = False,
+                           bidirectional = self.lstm_module.bidirectional,
+                           bias = self.lstm_module.bias,
+                           dropout = self.lstm_module.dropout)
+        # 正向
+        tmplayer.weight_ih_l0.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0.data, self.qwih)
+        tmplayer.weight_hh_l0.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0.data, self.qwhh)
+        tmplayer.bias_ih_l0.data = self.lstm_module.bias_ih_l0.data
+        tmplayer.bias_hh_l0.data = self.lstm_module.bias_hh_l0.data
+        # 反向
+        tmplayer.weight_ih_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_ih_l0_reverse.data, self.qwihr)
+        tmplayer.weight_hh_l0_reverse.data = FakeQuantize.apply(self.lstm_module.weight_hh_l0_reverse.data, self.qwhhr)
+        tmplayer.bias_ih_l0_reverse.data = self.lstm_module.bias_ih_l0_reverse.data
+        tmplayer.bias_hh_l0_reverse.data = self.lstm_module.bias_hh_l0_reverse.data
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        tmplayer.to(device)
+
+        if self.has_hidden and hidden is not None:
+            x, (h,c) = tmplayer(x,(h,c))
         else:
-            mean = Variable(self.bn_module.running_mean)
-            var = Variable(self.bn_module.running_var)
-
-        std = torch.sqrt(var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(mean, std)
+            x, (h,c) = tmplayer(x)
 
+        if hasattr(self,'qox'):
+            x = FakeQuantize.apply(x,self.qox)
+        if hasattr(self,'qoh'):
+            h = FakeQuantize.apply(h,self.qoh)
+        if hasattr(self,'qoc'):
+            c = FakeQuantize.apply(c,self.qoc)
         
-        x = F.conv2d(x, weight, bias, 
-                stride=self.conv_module.stride,
-                padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
-                groups=self.conv_module.groups)
-
-        return x
-    
-    def quantize_inference(self, x):
-        x = self.conv_module(x)  
-        return x
+        hidden = (h,c)
+        return x, hidden
 
         
-class ConvBNReLU6(nn.Module):
-    def __init__(self,conv_module, bn_module):
-        super(ConvBNReLU6, self).__init__()
-        self.conv_module = conv_module
-        self.bn_module = bn_module
 
-    def fold_bn(self, mean, std):
-        if self.bn_module.affine:
-            gamma_ = self.bn_module.weight / std
-            weight = self.conv_module.weight * gamma_.view(self.conv_module.out_channels, 1, 1, 1)
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean + self.bn_module.bias
-            else:
-                bias = self.bn_module.bias - gamma_ * mean
-        else:
-            gamma_ = 1 / std
-            weight = self.conv_module.weight * gamma_
-            if self.conv_module.bias is not None:
-                bias = gamma_ * self.conv_module.bias - gamma_ * mean
-            else:
-                bias = -gamma_ * mean
-            
-        return weight, bias
-
-    def freeze(self):
-
-        std = torch.sqrt(self.bn_module.running_var + self.bn_module.eps)
+class QEmbedding(QModule):
+    def __init__(self,quant_type,embedding_module,qi=False, qo=True, num_bits=8, e_bits=3):
+        super(QEmbedding, self).__init__(quant_type,qi,qo, num_bits, e_bits)
+        self.embedding_module = embedding_module
+        self.qw = QParam(quant_type, num_bits, e_bits)
+        self.register_buffer('M', torch.tensor([], requires_grad=False)) 
 
-        weight, bias = self.fold_bn(self.bn_module.running_mean, std)
+    def freeze(self, qo=None):
+        #输入为index，不量化
+        if hasattr(self, 'qo') and qo is not None:
+            raise ValueError('qo has been provided in init function.')
+        if not hasattr(self, 'qo') and qo is None:
+            raise ValueError('qo is not existed, should be provided.')
 
-        self.conv_module.weight.data = weight.data
-        if self.conv_module.bias is None:
-            self.conv_module.bias = nn.Parameter(bias)
-        else:
-            self.conv_module.bias.data = bias        
+        if qo is not None:
+            self.qo = qo
 
-    def fakefreeze(self):
-        pass
+        self.M.data = (self.qw.scale / self.qo.scale).data
         
+        self.embedding_module.weight.data = self.qw.quantize_tensor(self.embedding_module.weight.data)
         
     def forward(self, x):
+        self.qw.update(self.embedding_module.weight.data)
+        tmp_wgt = FakeQuantize.apply(self.embedding_module.weight, self.qw)
+        # tmp_wgt = self.embedding_module.weight
+        x = F.embedding(x, tmp_wgt)
 
-        if self.training:
-            y = F.conv2d(x, self.conv_module.weight, self.conv_module.bias, 
-                            stride=self.conv_module.stride,
-                            padding=self.conv_module.padding,
-                            dilation=self.conv_module.dilation,
-                            groups=self.conv_module.groups)
-            y = y.permute(1, 0, 2, 3) # NCHW -> CNHW
-            y = y.contiguous().view(self.conv_module.out_channels, -1) # CNHW -> C,NHW
-            # mean = y.mean(1)
-            # var = y.var(1)
-            mean = y.mean(1).detach()
-            var = y.var(1).detach()
-            self.bn_module.running_mean = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_mean + \
-                self.bn_module.momentum * mean
-            self.bn_module.running_var = \
-                (1 - self.bn_module.momentum) * self.bn_module.running_var + \
-                self.bn_module.momentum * var
-        else:
-            mean = Variable(self.bn_module.running_mean)
-            var = Variable(self.bn_module.running_var)
-
-        std = torch.sqrt(var + self.bn_module.eps)
-
-        weight, bias = self.fold_bn(mean, std)
-
-
-        x = F.conv2d(x, weight, bias, 
-                stride=self.conv_module.stride,
-                padding=self.conv_module.padding, dilation=self.conv_module.dilation, 
-                groups=self.conv_module.groups)
-
-        x = F.relu6(x)
+        if hasattr(self, 'qo'):
+            self.qo.update(x)
+            x = FakeQuantize.apply(x, self.qo)
         
         return x
 
     def quantize_inference(self, x):
-        x = self.conv_module(x)  
-        x.clamp_(min=0,max=6)
+        # x = self.embedding_module(x)
+        # x = self.M * x
+        # x = get_nearest_val(self.quant_type, x)
+        # return x
+    
+        tmp_wgt = FakeQuantize.apply(self.embedding_module.weight, self.qw)
+        # tmp_wgt = self.embedding_module.weight
+        x = F.embedding(x, tmp_wgt)
+        x = FakeQuantize.apply(x, self.qo)
         return x
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/param_flops_lstm-ocr.txt

+CRNN(
+  11.77 M, 100.000% Params, 1.26 GMac, 100.000% MACs, 
+  (layers): ModuleDict(
+    11.77 M, 100.000% Params, 1.26 GMac, 100.000% MACs, 
+    (conv1): Conv2d(640, 0.005% Params, 3.28 MMac, 0.260% MACs, 1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (relu1): ReLU(0, 0.000% Params, 327.68 KMac, 0.026% MACs, )
+    (pool1): MaxPool2d(0, 0.000% Params, 327.68 KMac, 0.026% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+    (conv2): Conv2d(73.86 k, 0.628% Params, 94.54 MMac, 7.494% MACs, 64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (relu2): ReLU(0, 0.000% Params, 163.84 KMac, 0.013% MACs, )
+    (pool2): MaxPool2d(0, 0.000% Params, 163.84 KMac, 0.013% MACs, kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+    (conv3): Conv2d(295.17 k, 2.508% Params, 94.45 MMac, 7.487% MACs, 128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn3): BatchNorm2d(512, 0.004% Params, 163.84 KMac, 0.013% MACs, 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (relu3): ReLU(0, 0.000% Params, 81.92 KMac, 0.006% MACs, )
+    (conv4): Conv2d(590.08 k, 5.014% Params, 188.83 MMac, 14.968% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (relu4): ReLU(0, 0.000% Params, 81.92 KMac, 0.006% MACs, )
+    (pool4): MaxPool2d(0, 0.000% Params, 81.92 KMac, 0.006% MACs, kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
+    (conv5): Conv2d(1.18 M, 10.029% Params, 193.55 MMac, 15.342% MACs, 256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn5): BatchNorm2d(1.02 k, 0.009% Params, 167.94 KMac, 0.013% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (relu5): ReLU(0, 0.000% Params, 83.97 KMac, 0.007% MACs, )
+    (conv6): Conv2d(2.36 M, 20.053% Params, 387.01 MMac, 30.677% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (relu6): ReLU(0, 0.000% Params, 83.97 KMac, 0.007% MACs, )
+    (pool6): MaxPool2d(0, 0.000% Params, 83.97 KMac, 0.007% MACs, kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
+    (conv7): Conv2d(1.05 M, 8.915% Params, 43.01 MMac, 3.409% MACs, 512, 512, kernel_size=(2, 2), stride=(1, 1))
+    (bn7): BatchNorm2d(1.02 k, 0.009% Params, 41.98 KMac, 0.003% MACs, 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (relu7): ReLU(0, 0.000% Params, 20.99 KMac, 0.002% MACs, )
+    (lstm1): LSTM(1.58 M, 13.401% Params, 64.87 MMac, 5.142% MACs, 512, 256, bidirectional=True)
+    (fc1): Linear(131.33 k, 1.116% Params, 5.37 MMac, 0.426% MACs, in_features=512, out_features=256, bias=True)
+    (lstm2): LSTM(1.05 M, 8.945% Params, 43.37 MMac, 3.438% MACs, 256, 256, bidirectional=True)
+    (fc2): Linear(3.46 M, 29.364% Params, 141.41 MMac, 11.209% MACs, in_features=512, out_features=6736, bias=True)
+  )
+)
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/ptq.py

 # -*- coding: utf-8 -*-
 from torch.serialization import load
 from model import *
-# from extract_ratio import *
+from extract_ratio import *
+
+from train import parse_arg
+from model import *
 from utils import *
+from lstm_utils import *
+from dataset import get_dataset
+from config.alphabets import *
+from torch.utils.data import DataLoader
 
 import gol
 import openpyxl
@@ -18,161 +25,117 @@ import torch.utils.bottleneck as bn
 import os
 import os.path as osp
 from torch.utils.tensorboard import SummaryWriter
-import json
-from decoder import seq_mnist_decoder
-from data import seq_mnist_train, seq_mnist_val
-from torch.utils.data import DataLoader
-import random
 
-class objdict(dict):
-    def __getattr__(self, name):
-        if name in self:
-            return self[name]
-        else:
-            raise AttributeError("No such attribute: " + name)
 
-    def __setattr__(self, name, value):
-        self[name] = value
+def direct_quantize(config, val_loader, dataset, converter, model, criterion, device):
 
-    def __delattr__(self, name):
-        if name in self:
-            del self[name]
-        else:
-            raise AttributeError("No such attribute: " + name)
-
-
-# def direct_quantize(model, test_loader,device):
-#     for i, (data, target) in enumerate(test_loader, 1):
-#         data = data.to(device)
-#         output = model.quantize_forward(data).cpu()
-#         if i % 500 == 0:
-#             break
-#     print('direct quantization finish')
-
-
-# def full_inference(model, test_loader, device):
-#     correct = 0
-#     for i, (data, target) in enumerate(test_loader, 1):
-#         data = data.to(device)
-#         output = model(data).cpu()
-#         pred = output.argmax(dim=1, keepdim=True)
-#         correct += pred.eq(target.view_as(pred)).sum().item()
-#     print('\nTest set: Full Model Accuracy: {:.2f}%'.format(100. * correct / len(test_loader.dataset)))
-#     return 100. * correct / len(test_loader.dataset)
-
-
-# def quantize_inference(model, test_loader, device):
-#     correct = 0
-#     for i, (data, target) in enumerate(test_loader, 1):
-#         data = data.to(device)
-#         output = model.quantize_inference(data).cpu()
-#         pred = output.argmax(dim=1, keepdim=True)
-#         correct += pred.eq(target.view_as(pred)).sum().item()
-#     print('Test set: Quant Model Accuracy: {:.2f}%'.format(100. * correct / len(test_loader.dataset)))
-#     return 100. * correct / len(test_loader.dataset)
-
-def direct_quantize(model, val_loader , val_data ,args , trainer_params, decoder, criterion):
     model.eval()
-    loss_value = 0
-    for i, (item) in enumerate(val_loader):           
-        data, labels, output_len, lab_len = item
 
-        data = Variable(data.transpose(1,0), requires_grad=False)
-        labels = Variable(labels.view(-1), requires_grad=False)
-        output_len = Variable(output_len.view(-1), requires_grad=False)
-        lab_len = Variable(lab_len.view(-1), requires_grad=False)
+    n_correct = 0
+    with torch.no_grad():
+        for i, (inp, idx) in enumerate(val_loader):
+            inp = inp.to(device)
+            # inference
+            preds = model.quantize_forward(inp)
 
+    print('direct quantization finish')
 
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-        data = data.to(device)
 
-        output = model.quantize_forward(data)
-#         if i % 500 == 0:
-# #             break
+def full_inference(config, val_loader, dataset, converter, model, criterion, device):
+    losses = AverageMeter()
+    model.eval()
 
-    print('direct quantization finish')
+    n_correct = 0
+    with torch.no_grad():
+        for i, (inp, idx) in enumerate(val_loader):
 
+            # 一个batch的label的list
+            labels = get_batch_label(dataset, idx)
+            inp = inp.to(device)
 
+            # inference
+            preds = model(inp).cpu()
 
-    # loss_value /= (len(val_data)//trainer_params.test_batch_size)
-    # # loss_value = loss_value[0]
-    # loss_value = loss_value.item()
-    # print("Average Loss Value for Val Data is = {:.4f}\n".format(float(loss_value)))
+            # compute loss
+            batch_size = inp.size(0)
+            text, length = converter.encode(labels)
+            preds_size = torch.IntTensor([preds.size(0)] * batch_size)
+            loss = criterion(preds, text, preds_size, length)
 
-def full_inference(model, val_loader , val_data ,args , trainer_params, decoder, criterion):
-    model.eval()
-    loss_value = 0
-    for i, (item) in enumerate(val_loader):           
-        data, labels, output_len, lab_len = item
+            losses.update(loss.item(), inp.size(0))
 
-        data = Variable(data.transpose(1,0), requires_grad=False)
-        labels = Variable(labels.view(-1), requires_grad=False)
-        output_len = Variable(output_len.view(-1), requires_grad=False)
-        lab_len = Variable(lab_len.view(-1), requires_grad=False)
+            _, preds = preds.max(2)
+            preds = preds.transpose(1, 0).contiguous().view(-1)
+            # sim_preds是decode后的，得到了一个str或者一个str list (也是一个batch的)
+            sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
+            # target应该是一个list  这里相当于在判断字符串是否相等
+            for pred, target in zip(sim_preds, labels):
+                if pred == target:
+                    n_correct += 1
 
+            # if (i + 1) % config.PRINT_FREQ == 0:
+            #     print('Epoch: [{0}][{1}/{2}]'.format(epoch, i, len(val_loader)))
 
-            # data = data.cuda()
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-        data = data.to(device)
+    # # 只打印展示前 config.TEST.NUM_TEST_DISP 个句子的对比
+    # raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.TEST.NUM_TEST_DISP]
+    # for raw_pred, pred, gt in zip(raw_preds, sim_preds, labels):
+    #     print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
 
-        output = model(data)
 
-        index = random.randint(0,trainer_params.test_batch_size-1)      
-        label = labels[index*trainer_params.word_size:(index+1)*trainer_params.word_size].data.numpy()
-        label = label-1
-        prediction = decoder.decode(output[:,index,:], output_len[index], lab_len[index])
-        accuracy = decoder.hit(prediction, label)
+    num_test_sample = len(dataset)
+    print("[#Full Precision Model: correct:{} / #total:{}]".format(n_correct, num_test_sample))
+    accuracy = n_correct / float(num_test_sample)
+    print('Full Precision Model: Test loss: {:.4f}, accuray: {:.4f}'.format(losses.avg, accuracy))
+    return accuracy
 
-        print("Sample Label      = {}".format(decoder.to_string(label))) 
-        print("Sample Prediction = {}".format(decoder.to_string(prediction)))
-        print("Full Model Accuracy on Sample = {:.2f}%\n\n".format(accuracy))
 
-        loss = criterion(output, labels, output_len, lab_len)
-        # loss_value += loss.data.numpy()
-        loss_value += loss.cpu().data.numpy()
 
+def quantize_inference(config, val_loader, dataset, converter, model, criterion, device):
+    losses = AverageMeter()
+    model.eval()
 
-    loss_value /= (len(val_data)//trainer_params.test_batch_size)
-    # loss_value = loss_value[0]
-    loss_value = loss_value.item()
-    print("Full Model Average Loss Value for Val Data is = {:.4f}\n".format(float(loss_value)))
+    n_correct = 0
+    with torch.no_grad():
+        for i, (inp, idx) in enumerate(val_loader):
 
-def quantize_inference(model, val_loader , val_data ,args , trainer_params, decoder, criterion):
-    model.eval()
-    loss_value = 0
-    for i, (item) in enumerate(val_loader):           
-        data, labels, output_len, lab_len = item
+            # 一个batch的label的list
+            labels = get_batch_label(dataset, idx)
+            inp = inp.to(device)
 
-        data = Variable(data.transpose(1,0), requires_grad=False)
-        labels = Variable(labels.view(-1), requires_grad=False)
-        output_len = Variable(output_len.view(-1), requires_grad=False)
-        lab_len = Variable(lab_len.view(-1), requires_grad=False)
+            # inference
+            preds = model.quantize_inference(inp).cpu()
 
-            # data = data.cuda()
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-        data = data.to(device)
+            # compute loss
+            batch_size = inp.size(0)
+            text, length = converter.encode(labels)
+            preds_size = torch.IntTensor([preds.size(0)] * batch_size)
+            loss = criterion(preds, text, preds_size, length)
 
-        output = model.quantize_inference(data)
+            losses.update(loss.item(), inp.size(0))
 
-        index = random.randint(0,trainer_params.test_batch_size-1)      
-        label = labels[index*trainer_params.word_size:(index+1)*trainer_params.word_size].data.numpy()
-        label = label-1
-        prediction = decoder.decode(output[:,index,:], output_len[index], lab_len[index])
-        accuracy = decoder.hit(prediction, label)
+            _, preds = preds.max(2)
+            preds = preds.transpose(1, 0).contiguous().view(-1)
+            # sim_preds是decode后的，得到了一个str或者一个str list (也是一个batch的)
+            sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
+            # target应该是一个list  这里相当于在判断字符串是否相等
+            for pred, target in zip(sim_preds, labels):
+                if pred == target:
+                    n_correct += 1
 
-        print("Sample Label      = {}".format(decoder.to_string(label))) 
-        print("Sample Prediction = {}".format(decoder.to_string(prediction)))
-        print("Quantize Model Accuracy on Sample = {:.2f}%\n\n".format(accuracy))
+    #         if (i + 1) % config.PRINT_FREQ == 0:
+    #             print('Epoch: [{0}][{1}/{2}]'.format(epoch, i, len(val_loader)))
 
-        loss = criterion(output, labels, output_len, lab_len)
-        # loss_value += loss.data.numpy()
-        loss_value += loss.cpu().data.numpy()
+    # # 只打印展示前 config.TEST.NUM_TEST_DISP 个句子的对比
+    # raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.TEST.NUM_TEST_DISP]
+    # for raw_pred, pred, gt in zip(raw_preds, sim_preds, labels):
+    #     print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
 
 
-    loss_value /= (len(val_data)//trainer_params.test_batch_size)
-    # loss_value = loss_value[0]
-    loss_value = loss_value.item()
-    print("Quantize Model Average Loss Value for Val Data is = {:.4f}\n".format(float(loss_value)))
+    num_test_sample = len(dataset)
+    print("[#Quantization Model: correct:{} / #total:{}]".format(n_correct, num_test_sample))
+    accuracy = n_correct / float(num_test_sample)
+    print('Quantization Model: Test loss: {:.4f}, accuray: {:.4f}'.format(losses.avg, accuracy))
+    return accuracy
 
 
 
@@ -190,86 +153,79 @@ def js_div(p_output, q_output, get_softmax=True):
 
 if __name__ == "__main__":
 
+    # load config
+    config = parse_arg()
 
-    parser = argparse.ArgumentParser(description='PyTorch FP32 Training')
-    parser.add_argument('-m', '--model', metavar='MODEL ARCH', default='ResNet18')
-    parser.add_argument('-b', '--batch_size', default=128, type=int, metavar='BATCH SIZE', help='mini-batch size (default: 128)')
-    parser.add_argument('-j','--workers', default=4, type=int, metavar='WORKERS',help='number of data loading workers (default: 4)')
-    parser.add_argument('-s', '--save',  default=False, type=bool)
-    # parser.add_argument('-t', '--test', dest='test', action='store_true', help='test model on test set')
-    parser.add_argument('--params', '-p', type=str, default="default_trainer_params.json", help='Path to params JSON file. Default ignored when resuming.')
-    # 训练参数
-    args = parser.parse_args()
-
-    with open(args.params) as d:
-        trainer_params = json.load(d)
-            # trainer_params = json.load(d, object_hook=ascii_encode_dict)
-    trainer_params = objdict(trainer_params)
-
-    batch_size = args.batch_size
-    num_workers = args.workers
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    print(device)
-
-    labels = [i for i in range(trainer_params.num_classes-1)]
-    decoder = seq_mnist_decoder(labels=labels)
-    criterion = nn.CTCLoss(blank=0, reduction='mean', zero_infinity=False)
-
-    random.seed(trainer_params.random_seed)
-    torch.manual_seed(trainer_params.random_seed)
-    # if args.cuda:
-    torch.cuda.manual_seed_all(trainer_params.random_seed)
+    # construct face related neural networks
+    model = get_crnn(config)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    train_data = seq_mnist_train(trainer_params)
-    val_data = seq_mnist_val(trainer_params) 
 
-    train_loader = DataLoader(train_data, batch_size=trainer_params.batch_size, \
-                                        shuffle=True, num_workers=trainer_params.num_workers)
+    # define loss function
+    criterion = torch.nn.CTCLoss()
 
-    val_loader = DataLoader(val_data, batch_size=trainer_params.test_batch_size, \
-                                        shuffle=False, num_workers=trainer_params.num_workers)      
+    train_dataset = get_dataset(config)(config, is_train=True)
+    train_loader = DataLoader(
+        dataset=train_dataset,
+        batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
+        shuffle=config.TRAIN.SHUFFLE,
+        num_workers=config.WORKERS,
+        pin_memory=config.PIN_MEMORY,
+    )
 
-    if args.model == 'LSTM-OCR':
-        model = BiLSTM(trainer_params)
+    val_dataset = get_dataset(config)(config, is_train=False)
+    val_loader = DataLoader(
+        dataset=val_dataset,
+        batch_size=config.TEST.BATCH_SIZE_PER_GPU,
+        shuffle=config.TEST.SHUFFLE,
+        num_workers=config.WORKERS,
+        pin_memory=config.PIN_MEMORY,
+    )
 
+    converter = strLabelConverter(config.DATASET.ALPHABETS)
 
-    # writer = SummaryWriter(log_dir='log/' + args.model  +  '/ptq')
-    save_dir = 'ckpt'
-    full_file = save_dir + '/mnist_' + trainer_params.reduce_bidirectional +'_' + str(trainer_params.bidirectional) + '.pt'
+    writer = SummaryWriter(log_dir='log/' + config.MODEL.NAME  +  '/ptq')
+    full_file = 'ckpt/360cc_' + config.MODEL.NAME + '.pt'
     model.load_state_dict(torch.load(full_file))
     model.to(device)
 
-    load_ptq = False
-    ptq_file_prefix = 'ckpt/mnist_' + trainer_params.reduce_bidirectional +'_' + str(trainer_params.bidirectional)  + '_ptq_'
+    load_ptq = True
+    ptq_file_prefix = 'ckpt/360cc_' + config.MODEL.NAME + '_ptq_'
 
     model.eval()
-    full_acc = full_inference(model, val_loader, val_data, args, trainer_params, decoder,criterion)
+    full_acc = full_inference(config, val_loader, val_dataset, converter, model, criterion, device)
 
-    # model_fold = fold_model(model)   # 
   
-    # full_params = []
+    model_fold = fold_model(model)   # 可以得到conv，bn，relu，fc的各个层 
     
-    # layer, par_ratio, flop_ratio = extract_ratio(args.model)
     
-    # layer = []
-    # for name, param in model.named_parameters():
-    #     if 'weight' in name:
-    #         n = name.split('.')
-    #         pre = '.'.join(n[:len(n)-1])
-    #         # 提取出weight前的名字(就是这个层的名字，if weight是避免bias重复提取一遍名字)
-    #         layer.append(pre)
+    full_params = []
 
-    # print('===================')
+    layer, par_ratio, flop_ratio = extract_ratio('lstm-ocr')
+    # print(layer)
+    layer = []
+    for name, param in model.named_parameters():
+        # 提取出weight前的名字(就是这个层的名字)
+        n = name.split('.')
+        pre = '.'.join(n[:len(n)-1])
+        # 避免重复添加
+        if pre not in layer:
+            layer.append(pre)
         
-    # par_ratio, flop_ratio = fold_ratio(layer, par_ratio, flop_ratio)
 
-    # for name, param in model_fold.named_parameters():
-    #     if 'bn' in name or 'sample.1' in name:
-    #         continue
-    #     param_norm = param.data.cpu()
-    #     full_params.append(param_norm)  # 没统计bn的 只统计了conv的 而且还是fold后的
-    #     writer.add_histogram(tag='Full_' + name + '_data', values=param.data)
+    print('===================')
+    # print(layer)
+    par_ratio, flop_ratio = fold_ratio(layer, par_ratio, flop_ratio)
+    # sys.exit()
+    for name, param in model_fold.named_parameters():
+        if 'bn' in name or 'sample.1' in name:
+            continue
+        # param_norm = F.normalize(param.data.cpu(),p=2,dim=-1)
+        param_data = param.data.cpu()
+        full_params.append(param_data)  # 统计了fold后的conv以及fc的weight和bias
+        writer.add_histogram(tag='Full_' + name + '_data', values=param.data)
+
+        # print(f"full-precision after Fold: name:{name}, param:{param.data}, param_data:{param_data}")
     
     
     gol._init()
@@ -291,10 +247,10 @@ if __name__ == "__main__":
         for num_bits in num_bit_list:
             e_bit_list = ebit_list(quant_type,num_bits)
             for e_bits in e_bit_list:
-                # model_ptq = resnet18()
-                if args.model == 'LSTM-OCR':
-                    model_ptq = BiLSTM(trainer_params)
-
+                # model_ptq = MobileNetV2()
+                model_ptq = get_crnn(config)
+                device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+                model_ptq = model_ptq.to(device)
 
                 if quant_type == 'FLOAT':
                     title = '%s_%d_E%d' % (quant_type, num_bits, e_bits)
@@ -319,59 +275,62 @@ if __name__ == "__main__":
                     model_ptq.to(device)
                     model_ptq.quantize(quant_type,num_bits,e_bits)
                     model_ptq.eval()
-                    direct_quantize(model_ptq, val_loader, val_data, args, trainer_params, decoder,criterion)
-
+                    direct_quantize(config, val_loader, val_dataset, converter, model_ptq, criterion, device)
                     # if args.save == True:
                     #     torch.save(model_ptq.state_dict(), ptq_file_prefix + title + '.pt')
 
-
                 model_ptq.freeze()
-                quantize_inference(model_ptq, val_loader, val_data, args, trainer_params, decoder,criterion)
-                
-                # ptq_acc = quantize_inference(model_ptq, val_loader, val_data, args, trainer_params, decoder,criterion)
-                # ptq_acc_list.append(ptq_acc)
-                # acc_loss = (full_acc - ptq_acc) / full_acc
-                # acc_loss_list.append(acc_loss)
-                # idx = -1
-
+                ptq_acc = quantize_inference(config, val_loader, val_dataset, converter, model_ptq, criterion, device)
+                ptq_acc_list.append(ptq_acc)
+                acc_loss = (full_acc - ptq_acc) / full_acc
+                acc_loss_list.append(acc_loss)
+                idx = -1
+
+                # 对权值参数反量化
+                model_ptq.fakefreeze()
                 # 获取计算量/参数量下的js-div
                 js_flops = 0.
                 js_param = 0.
+                for name, param in model_ptq.named_parameters():
+                    # if '.' not in name or 'bn' in name:
+                    if 'bn' in name or 'sample.1' in name:
+                        continue
+                    writer.add_histogram(tag=title +':'+ name + '_data', values=param.data)
+                    idx = idx + 1
+                    # renset中有多个. 需要改写拼一下
+                    # prefix = name.split('.')[0]
+                    n = name.split('.')
+                    prefix = '.'.join(n[:len(n) - 1])
+                     # weight和bias 1:1 ? 对于ratio，是按层赋予的，此处可以对weight和bias再单独赋予不同的权重，比如(8:2)
+                     # layer中只有conv,bn,fc, prefix只可能是conv，fc (顺序的，weight和bias都有)
+                    if prefix in layer:
+                        layer_idx = layer.index(prefix)
+                        ptq_param = param.data.cpu()
+                        # 取L2范数
+                        # ptq_norm = F.normalize(ptq_param,p=2,dim=-1)
+                        writer.add_histogram(tag=title +':'+ name + '_data', values=ptq_param)
+                        # print(name)
+                        # print('=========')
+                        # print(ptq_norm)
+                        # print('=========')
+                        # print(full_params[idx])
+
+                        # full_params中只有conv和fc 且是顺序下来的(weight和bias都有)
+                        js = js_div(ptq_param,full_params[idx])   # 这里算了fold后的量化前后模型的js距离
+                        js = js.item()
+                        origin_js = js
+                        # print()                               
+                        if js < 0.:
+                            js = 0.
+                        js_flops = js_flops + js * flop_ratio[layer_idx]
+                        js_param = js_param + js * par_ratio[layer_idx]
+                        print(f"prefix:{prefix}, layer_idx:{layer_idx}")
+                        # print(f"layer name:{prefix} layer_idx is:{layer_idx}  origin_js is {origin_js}  js is {js}  flop_ratio is {flop_ratio[layer_idx]}  par_ratio is {par_ratio[layer_idx]}  js_flops is {js_flops}  js_param is {js_param}")
+                js_flops_list.append(js_flops)
+                js_param_list.append(js_param)
+
+                print(title + ': js_flops: %f js_param: %f acc_loss: %f' % (js_flops, js_param, acc_loss))
     
-                # for name, param in model_ptq.named_parameters():
-                #     # if '.' not in name or 'bn' in name:
-                #     if 'bn' in name or 'sample.1' in name:
-                #         continue
-                #     writer.add_histogram(tag=title +':'+ name + '_data', values=param.data)
-                #     idx = idx + 1
-                #     # renset中有多个. 需要改写拼一下
-                #     # prefix = name.split('.')[0]
-                #     n = name.split('.')
-                #     prefix = '.'.join(n[:len(n) - 1])
-                #      # weight和bias 1:1 ? 对于ratio，是按层赋予的，此处可以对weight和bias再单独赋予不同的权重，比如(8:2)
-                #     if prefix in layer:
-                #         layer_idx = layer.index(prefix)
-                #         ptq_param = param.data.cpu()
-                #         # 取L2范数
-                #         # ptq_norm = F.normalize(ptq_param,p=2,dim=-1)
-                #         ptq_norm = ptq_param
-                #         writer.add_histogram(tag=title +':'+ name + '_data', values=ptq_param)
-                #         # print(name)
-                #         # print('=========')
-                #         # print(ptq_norm)
-                #         # print('=========')
-                #         # print(full_params[idx])
-                #         js = js_div(ptq_norm,full_params[idx])   # 这里算了fold后的量化前后模型的js距离
-                #         js = js.item()
-                #         if js < 0.:
-                #             js = 0.
-                #         js_flops = js_flops + js * flop_ratio[layer_idx]
-                #         js_param = js_param + js * par_ratio[layer_idx]
-                # js_flops_list.append(js_flops)
-                # js_param_list.append(js_param)
-
-                # print(title + ': js_flops: %f js_param: %f acc_loss: %f' % (js_flops, js_param, acc_loss))
-    sys.exit()
     # 写入xlsx
     workbook = openpyxl.Workbook()
     worksheet = workbook.active
@@ -389,10 +348,10 @@ if __name__ == "__main__":
         worksheet.cell(row=i+4, column=4, value=ptq_acc_list[i])
         worksheet.cell(row=i+4, column=5, value=acc_loss_list[i])
     
-    workbook.save('ptq_result_' + args.model + '.xlsx')
+    workbook.save('ptq_result_' + config.MODEL.NAME + '.xlsx')
     writer.close()
 
-    ft = open('ptq_result_' + args.model + '.txt','w')
+    ft = open('ptq_result_' + config.MODEL.NAME + '.txt','w')
 
     print('title_list:',file=ft)
     print(" ".join(title_list),file=ft)

mzh/new_mzh/LSTM-OCR/ptq_result_lstm-ocr.txt

+title_list:
+INT_2 INT_3 INT_4 INT_5 INT_6 INT_7 INT_8 INT_9 INT_10 INT_11 INT_12 INT_13 INT_14 INT_15 INT_16 POT_2 POT_3 POT_4 POT_5 POT_6 POT_7 POT_8 FLOAT_3_E1 FLOAT_4_E1 FLOAT_4_E2 FLOAT_5_E1 FLOAT_5_E2 FLOAT_5_E3 FLOAT_6_E1 FLOAT_6_E2 FLOAT_6_E3 FLOAT_6_E4 FLOAT_7_E1 FLOAT_7_E2 FLOAT_7_E3 FLOAT_7_E4 FLOAT_7_E5 FLOAT_8_E1 FLOAT_8_E2 FLOAT_8_E3 FLOAT_8_E4 FLOAT_8_E5 FLOAT_8_E6
+js_flops_list:
+938.7733335552051 422.2961529040284 112.00572935619252 28.15632049948652 6.580025053557225 1.593147596698573 0.39127389747221836 0.09726359211545822 0.02424534011691382 0.006096811349829773 0.0015819717562818567 0.0004088973671629054 0.00010591612996765123 4.215430786749398e-05 3.358851529044255e-05 938.0795941095593 276.1298308688724 43.56510410351131 43.31743097440866 43.31651992748872 43.317170796987085 43.316909690885424 245.27340648023244 80.55429874312716 50.01809924502505 38.66623068838267 17.323188075553762 12.265772098808744 26.78403199897619 9.167642811282255 3.1779184189380314 12.23167946700013 22.667666751272108 6.595860316723723 0.8016993676302422 3.1649891963442665 12.231686340919987 21.030391880048075 5.630180110297391 0.20824827230948417 0.7939111200807045 3.164995719146672 12.23168958358928
+js_param_list:
+1083.2152054453256 407.68102883090796 142.77271018968833 40.694342955319094 9.528033474559528 2.3056188772063395 0.5663514708991146 0.1405112916168392 0.03504902772537398 0.008813186313906787 0.0022363630716486275 0.000566627677334268 0.00014559649495622196 4.1442517690921754e-05 3.062477331841085e-05 1083.0293894297583 286.18087410960214 48.04799916302135 47.56581240023642 47.5640587789577 47.565362072997914 47.56507051619791 306.72068771644035 110.02866499298202 52.861756413549614 51.56753626886545 17.228751722832826 13.602914776865827 35.21196010598172 8.706064889198597 3.4432562752359903 13.53603392058786 29.65906818846962 6.118510949236464 0.8693586052802382 3.417883320602458 13.536039876528541 27.482646086738573 5.183734622246258 0.22716556760816775 0.8541492170710019 3.417896091306703 13.536039813988335
+ptq_acc_list:
+0.0 0.0 0.0 0.172 0.627 0.772 0.7856666666666666 0.791 0.791 0.7936666666666666 0.793 0.7936666666666666 0.794 0.7936666666666666 0.794 0.0 0.0 0.087 0.09433333333333334 0.086 0.09533333333333334 0.09366666666666666 0.0 0.001 0.0033333333333333335 0.18733333333333332 0.343 0.591 0.4836666666666667 0.5843333333333334 0.7566666666666667 0.5906666666666667 0.557 0.657 0.7823333333333333 0.7503333333333333 0.5883333333333334 0.597 0.6843333333333333 0.792 0.785 0.7486666666666667 0.5933333333333334
+acc_loss_list:
+1.0 1.0 1.0 0.7834662190516157 0.21065883340327318 0.02811582039446074 0.010910616869492292 0.004196391103650818 0.004196391103650818 0.0008392782207302194 0.001678556441460299 0.0008392782207302194 0.00041963911036503983 0.0008392782207302194 0.00041963911036503983 1.0 1.0 0.8904741921947126 0.8812421317666806 0.8917331095258079 0.8799832144355854 0.8820814099874108 1.0 0.9987410826689047 0.9958036088963492 0.7641628199748216 0.5681913554343264 0.2559798573227025 0.39110365086026017 0.26437263953000417 0.04741921947125467 0.25639949643306753 0.2987830465799412 0.17289131347041542 0.01510700797314311 0.055392362568191404 0.25933697020562313 0.24842635333613097 0.13848090642047836 0.002937473772555558 0.011749895090222373 0.057490558120016744 0.2530423835501468

mzh/new_mzh/LSTM-OCR/readme.md

+## update 2023.5.14
+
+### 采用了CRNN的结构实现LSTM-OCR，相比于之前的版本有了更好、更通用的处理OCR问题的能力。我使用了360CC数据集(中文OCR训练数据集，共360w张图片，我只用了其中3w训练数据和3k张测试数据)来训练和测试模型。
+
+1. config文件夹里是配置文件:
+- alphabets.py是字典
+- 360CC_config.yaml是使用360CC数据集时的配置文件
+- OWN_config.yaml是使用其他数据集时的配置文件
+
+2. dataset文件夹:
+- txt文件夹中的char_std_5990.txt是360cc数据集的字典
+- test.txt中是配置测试集数据
+- train.txt中是配置训练集数据
+- _360cc.py用于构建360cc dataset来训练/测试
+- _own.py用于构建自己的dataset来训练/测试
+
+3. lstm_utils.py中是LSTM-OCR中需要用到的一些特定的函数、类等
+
+4. module.py中增加了对LSTM量化相关的组件，补充了对于BiLSTM的部分权值参数的伪量化，修改了QLinear的quantize_inference方法，使其仍然是真量化。
+
+5. train.py中的train和validate函数考虑了OCR问题的特点，有了较大修改。同理ptq.py中的direct_inference, full_inference, quantize_inference也有一定修改。
+
+6. 由于QLSTM采用伪量化，而其他层采用真量化，因此在model.py中与QLSTM直接连接的层需要通过dequantize_tensor(x)或quantize_tensor(x)来处理下数据。
+
+7. ptq_result_lstm-ocr.txt和ptq_result_lstm-ocr.xlsx是实验结果，仍采用acc作为评价指标，当一个句子被完全识别正确时，算是识别正确一次。
+
+8. 拟合图像如下 <br>
+flops:
+<img src = "fig/flops_lstmocr.png" class="h-90 auto">
+params:
+<img src = "fig/params_lstmocr.png" class="h-90 auto">
+
+
 
 ## update 2023.5.4
 

mzh/new_mzh/LSTM-OCR/train.py

+import argparse
+from easydict import EasyDict as edict
+import yaml
+import os
+import torch
+import torch.backends.cudnn as cudnn
+from torch.utils.data import DataLoader
+from model import *
+from utils import *
+from lstm_utils import *
+from dataset import get_dataset
+from config.alphabets import *
+import time
+import sys
+
+from torch.utils.tensorboard import SummaryWriter
+
+def parse_arg():
+    parser = argparse.ArgumentParser(description="train crnn")
+
+    # parser.add_argument('--cfg', help='experiment configuration filename', required=True, type=str)
+    parser.add_argument('--cfg', help='experiment configuration filename', type=str, default='config/360CC_config.yaml')
+
+    args = parser.parse_args()
+
+    with open(args.cfg, 'r') as f:
+        # config = yaml.load(f, Loader=yaml.FullLoader)
+        config = yaml.load(f,Loader=yaml.FullLoader)
+        config = edict(config)
+
+    config.DATASET.ALPHABETS = alphabet
+    config.MODEL.NUM_CLASSES = len(config.DATASET.ALPHABETS)
+
+    return config
+
+
+def train(config, train_loader, dataset, converter, model, criterion, optimizer, device, epoch):
+
+    batch_time = AverageMeter()
+    data_time = AverageMeter()
+    losses = AverageMeter()
+
+    model.train()
+
+    end = time.time()
+    for i, (inp, idx) in enumerate(train_loader):
+        # measure data time
+        data_time.update(time.time() - end)
+
+        labels = get_batch_label(dataset, idx)
+        inp = inp.to(device)
+
+        # inference
+        preds = model(inp).cpu()
+
+        # compute loss
+        batch_size = inp.size(0)
+        text, length = converter.encode(labels)                    # length = 一个batch中的总字符长度, text = 一个batch中的字符所对应的下标
+        preds_size = torch.IntTensor([preds.size(0)] * batch_size) # timestep * batchsize
+        loss = criterion(preds, text, preds_size, length)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        losses.update(loss.item(), inp.size(0))
+
+        batch_time.update(time.time()-end)
+        if i % config.PRINT_FREQ == 0:
+            msg = 'Epoch: [{0}][{1}/{2}]\t' \
+                  'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \
+                  'Speed {speed:.1f} samples/s\t' \
+                  'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \
+                  'Loss {loss.val:.5f} ({loss.avg:.5f})\t'.format(
+                      epoch, i, len(train_loader), batch_time=batch_time,
+                      speed=inp.size(0)/batch_time.val,
+                      data_time=data_time, loss=losses)
+            print(msg)
+
+        end = time.time()
+
+
+def validate(config, val_loader, dataset, converter, model, criterion, device, epoch):
+
+    losses = AverageMeter()
+    model.eval()
+
+    n_correct = 0
+    with torch.no_grad():
+        for i, (inp, idx) in enumerate(val_loader):
+
+            # 一个batch的label的list
+            labels = get_batch_label(dataset, idx)
+            inp = inp.to(device)
+
+            # inference
+            preds = model(inp).cpu()
+
+            # compute loss
+            batch_size = inp.size(0)
+            text, length = converter.encode(labels)
+            preds_size = torch.IntTensor([preds.size(0)] * batch_size)
+            loss = criterion(preds, text, preds_size, length)
+
+            losses.update(loss.item(), inp.size(0))
+
+            _, preds = preds.max(2)
+            preds = preds.transpose(1, 0).contiguous().view(-1)
+            # sim_preds是decode后的，得到了一个str或者一个str list (也是一个batch的)
+            sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
+            # target应该是一个list  这里相当于在判断字符串是否相等
+            for pred, target in zip(sim_preds, labels):
+                if pred == target:
+                    n_correct += 1
+
+            if (i + 1) % config.PRINT_FREQ == 0:
+                print('Epoch: [{0}][{1}/{2}]'.format(epoch, i, len(val_loader)))
+
+            # if i == config.TEST.NUM_TEST_BATCH:  # 只检查这些数据的  (config.TEST.NUM_TEST_BATCH个batch)
+            #     break
+
+    # 只打印展示前 config.TEST.NUM_TEST_DISP 个句子的对比
+    raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:config.TEST.NUM_TEST_DISP]
+    for raw_pred, pred, gt in zip(raw_preds, sim_preds, labels):
+        print('%-20s => %-20s, gt: %-20s' % (raw_pred, pred, gt))
+
+    # num_test_sample = config.TEST.NUM_TEST_BATCH * config.TEST.BATCH_SIZE_PER_GPU
+    # if num_test_sample > len(dataset):
+    #     num_test_sample = len(dataset)
+
+    num_test_sample = len(dataset)
+    print("[#correct:{} / #total:{}]".format(n_correct, num_test_sample))
+    accuracy = n_correct / float(num_test_sample)
+    print('Test loss: {:.4f}, accuray: {:.4f}'.format(losses.avg, accuracy))
+
+
+    return accuracy
+
+def main():
+
+    # load config
+    config = parse_arg()
+
+    # construct face related neural networks
+    model = get_crnn(config)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model = model.to(device)
+
+    # define loss function
+    criterion = torch.nn.CTCLoss()
+
+    # 上一轮训练的epoch  (起始为0 )
+    last_epoch = config.TRAIN.BEGIN_EPOCH
+
+
+    train_dataset = get_dataset(config)(config, is_train=True)
+    train_loader = DataLoader(
+        dataset=train_dataset,
+        batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
+        shuffle=config.TRAIN.SHUFFLE,
+        num_workers=config.WORKERS,
+        pin_memory=config.PIN_MEMORY,
+    )
+
+    val_dataset = get_dataset(config)(config, is_train=False)
+    val_loader = DataLoader(
+        dataset=val_dataset,
+        batch_size=config.TEST.BATCH_SIZE_PER_GPU,
+        shuffle=config.TEST.SHUFFLE,
+        num_workers=config.WORKERS,
+        pin_memory=config.PIN_MEMORY,
+    )
+
+    optimizer = get_optimizer(config, model)
+    if isinstance(config.TRAIN.LR_STEP, list):
+        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, config.TRAIN.LR_STEP,
+            config.TRAIN.LR_FACTOR, last_epoch-1
+        )
+    else:
+        lr_scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer, config.TRAIN.LR_STEP,
+            config.TRAIN.LR_FACTOR, last_epoch - 1
+        )
+
+
+    save_dir = 'ckpt'
+    if not os.path.isdir(save_dir):
+        os.makedirs(save_dir, mode=0o777)
+        os.chmod(save_dir, mode=0o777)
+        
+    converter = strLabelConverter(config.DATASET.ALPHABETS)
+
+    # for resume, only 
+    if config.TRAIN.RESUME.IS_RESUME:       
+       model.load_state_dict(torch.load(save_dir + '/360cc_' + config.MODEL.NAME + '.pt'))
+       acc = validate(config, val_loader, val_dataset, converter, model, criterion, device, config.TRAIN.END_EPOCH)
+       print(f"test accuracy: {acc:.2f}%")
+       sys.exit()
+
+
+
+    best_acc = 0.0
+    
+    for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
+
+        # train
+        train(config, train_loader, train_dataset, converter, model, criterion, optimizer, device, epoch)
+        lr_scheduler.step()
+
+        # validate
+        acc = validate(config, val_loader, val_dataset, converter, model, criterion, device, epoch)
+
+        # save best ckpt
+        if config.TRAIN.SAVE and acc>best_acc:
+           torch.save(model.state_dict(), save_dir + '/360cc_' + config.MODEL.NAME + '.pt')
+
+        best_acc = max(acc, best_acc)
+        print("best acc is:", best_acc)
+
+if __name__ == '__main__':
+
+    main()
\ No newline at end of file

mzh/new_mzh/LSTM-OCR/utils.py

 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torch.optim as optim
+
 
 def js_div(p_output, q_output, get_softmax=True):
     """
@@ -167,3 +169,5 @@ def fold_bn(conv, bn):
     
 
     return conv
+
+