First model version

app.py

@@ -25,9 +25,6 @@ def infer(filepath):
     )
     image = cv2.imread(filepath)
     result_polygons, result_masks, result_boxes = det_demo.run_on_opencv_image(image)
-    patchs = [image[box[1]:box[3], box[0]:box[2], :] for box in result_boxes]
-    patchs = [cv2.resize(patch, (128,32)) for patch in patchs]
-    patchs = np.stack(patchs, axis=0).transpose(0,3,1,2)
     visual_image = det_demo.visualization(image.copy(), result_polygons, result_masks, result_boxes)
     cv2.imwrite('result.jpg', visual_image)
     return 'result.jpg'#, pd.DataFrame(result_words)

data/charset_36.txt

@@ -0,0 +1,36 @@
+0	a
+1	b
+2	c
+3	d
+4	e
+5	f
+6	g
+7	h
+8	i
+9	j
+10	k
+11	l
+12	m
+13	n
+14	o
+15	p
+16	q
+17	r
+18	s
+19	t
+20	u
+21	v
+22	w
+23	x
+24	y
+25	z
+26	1
+27	2
+28	3
+29	4
+30	5
+31	6
+32	7
+33	8
+34	9
+35	0

data/charset_62.txt

@@ -0,0 +1,62 @@
+0	0
+1	1
+2	2
+3	3
+4	4
+5	5
+6	6
+7	7
+8	8
+9	9
+10	A
+11	B
+12	C
+13	D
+14	E
+15	F
+16	G
+17	H
+18	I
+19	J
+20	K
+21	L
+22	M
+23	N
+24	O
+25	P
+26	Q
+27	R
+28	S
+29	T
+30	U
+31	V
+32	W
+33	X
+34	Y
+35	Z
+36	a
+37	b
+38	c
+39	d
+40	e
+41	f
+42	g
+43	h
+44	i
+45	j
+46	k
+47	l
+48	m
+49	n
+50	o
+51	p
+52	q
+53	r
+54	s
+55	t
+56	u
+57	v
+58	w
+59	x
+60	y
+61	z

demo.py

@@ -0,0 +1,109 @@
+import argparse
+import logging
+import os
+import glob
+import tqdm
+import torch
+import PIL
+import cv2
+import numpy as np
+import torch.nn.functional as F
+from torchvision import transforms
+from utils import Config, Logger, CharsetMapper
+
+def get_model(config):
+    import importlib
+    names = config.model_name.split('.')
+    module_name, class_name = '.'.join(names[:-1]), names[-1]
+    cls = getattr(importlib.import_module(module_name), class_name)
+    model = cls(config)
+    logging.info(model)
+    model = model.eval()
+    return model
+
+def preprocess(img, width, height):
+    img = cv2.resize(np.array(img), (width, height))
+    img = transforms.ToTensor()(img).unsqueeze(0)
+    mean = torch.tensor([0.485, 0.456, 0.406])
+    std  = torch.tensor([0.229, 0.224, 0.225])
+    return (img-mean[...,None,None]) / std[...,None,None]
+
+def postprocess(output, charset, model_eval):
+    def _get_output(last_output, model_eval):
+        if isinstance(last_output, (tuple, list)): 
+            for res in last_output:
+                if res['name'] == model_eval: output = res
+        else: output = last_output
+        return output
+
+    def _decode(logit):
+        """ Greed decode """
+        out = F.softmax(logit, dim=2)
+        pt_text, pt_scores, pt_lengths = [], [], []
+        for o in out:
+            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
+            text = text.split(charset.null_char)[0]  # end at end-token
+            pt_text.append(text)
+            pt_scores.append(o.max(dim=1)[0])
+            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
+        return pt_text, pt_scores, pt_lengths
+
+    output = _get_output(output, model_eval)
+    logits, pt_lengths = output['logits'], output['pt_lengths']
+    pt_text, pt_scores, pt_lengths_ = _decode(logits)
+    
+    return pt_text, pt_scores, pt_lengths_
+
+def load(model, file, device=None, strict=True):
+    if device is None: device = 'cpu'
+    elif isinstance(device, int): device = torch.device('cuda', device)
+    assert os.path.isfile(file)
+    state = torch.load(file, map_location=device)
+    if set(state.keys()) == {'model', 'opt'}:
+        state = state['model']
+    model.load_state_dict(state, strict=strict)
+    return model
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, default='configs/train_abinet.yaml',
+                        help='path to config file')
+    parser.add_argument('--input', type=str, default='figs/test')
+    parser.add_argument('--cuda', type=int, default=-1)
+    parser.add_argument('--checkpoint', type=str, default='workdir/train-abinet/best-train-abinet.pth')
+    parser.add_argument('--model_eval', type=str, default='alignment', 
+                        choices=['alignment', 'vision', 'language'])
+    args = parser.parse_args()
+    config = Config(args.config)
+    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
+    if args.model_eval is not None: config.model_eval = args.model_eval
+    config.global_phase = 'test'
+    config.model_vision_checkpoint, config.model_language_checkpoint = None, None
+    device = 'cpu' if args.cuda < 0 else f'cuda:{args.cuda}'
+
+    Logger.init(config.global_workdir, config.global_name, config.global_phase)
+    Logger.enable_file()
+    logging.info(config)
+
+    logging.info('Construct model.')
+    model = get_model(config).to(device)
+    model = load(model, config.model_checkpoint, device=device)
+    charset = CharsetMapper(filename=config.dataset_charset_path,
+                            max_length=config.dataset_max_length + 1)
+
+    if os.path.isdir(args.input):
+        paths = [os.path.join(args.input, fname) for fname in os.listdir(args.input)]
+    else:
+        paths = glob.glob(os.path.expanduser(args.input))
+        assert paths, "The input path(s) was not found"
+    paths = sorted(paths)
+    for path in tqdm.tqdm(paths):
+        img = PIL.Image.open(path).convert('RGB')
+        img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
+        img = img.to(device)
+        res = model(img)
+        pt_text, _, __ = postprocess(res, charset, config.model_eval)
+        logging.info(f'{path}: {pt_text[0]}')
+
+if __name__ == '__main__':
+    main()

modules/attention.py

@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+from .transformer import PositionalEncoding
+
+class Attention(nn.Module):
+    def __init__(self, in_channels=512, max_length=25, n_feature=256):
+        super().__init__()
+        self.max_length = max_length
+
+        self.f0_embedding = nn.Embedding(max_length, in_channels)
+        self.w0 = nn.Linear(max_length, n_feature)
+        self.wv = nn.Linear(in_channels, in_channels)
+        self.we = nn.Linear(in_channels, max_length)
+
+        self.active = nn.Tanh()
+        self.softmax = nn.Softmax(dim=2)
+
+    def forward(self, enc_output):
+        enc_output = enc_output.permute(0, 2, 3, 1).flatten(1, 2)
+        reading_order = torch.arange(self.max_length, dtype=torch.long, device=enc_output.device)
+        reading_order = reading_order.unsqueeze(0).expand(enc_output.size(0), -1)  # (S,) -> (B, S)
+        reading_order_embed = self.f0_embedding(reading_order)  # b,25,512
+
+        t = self.w0(reading_order_embed.permute(0, 2, 1))  # b,512,256
+        t = self.active(t.permute(0, 2, 1) + self.wv(enc_output))  # b,256,512
+
+        attn = self.we(t)  # b,256,25
+        attn = self.softmax(attn.permute(0, 2, 1))  # b,25,256
+        g_output = torch.bmm(attn, enc_output)  # b,25,512
+        return g_output, attn.view(*attn.shape[:2], 8, 32)
+
+
+def encoder_layer(in_c, out_c, k=3, s=2, p=1):
+    return nn.Sequential(nn.Conv2d(in_c, out_c, k, s, p),
+                         nn.BatchNorm2d(out_c),
+                         nn.ReLU(True))
+
+def decoder_layer(in_c, out_c, k=3, s=1, p=1, mode='nearest', scale_factor=None, size=None):
+    align_corners = None if mode=='nearest' else True
+    return nn.Sequential(nn.Upsample(size=size, scale_factor=scale_factor, 
+                                     mode=mode, align_corners=align_corners),
+                         nn.Conv2d(in_c, out_c, k, s, p),
+                         nn.BatchNorm2d(out_c),
+                         nn.ReLU(True))
+
+
+class PositionAttention(nn.Module):
+    def __init__(self, max_length, in_channels=512, num_channels=64, 
+                 h=8, w=32, mode='nearest', **kwargs):
+        super().__init__()
+        self.max_length = max_length
+        self.k_encoder = nn.Sequential(
+            encoder_layer(in_channels, num_channels, s=(1, 2)),
+            encoder_layer(num_channels, num_channels, s=(2, 2)),
+            encoder_layer(num_channels, num_channels, s=(2, 2)),
+            encoder_layer(num_channels, num_channels, s=(2, 2))
+        )
+        self.k_decoder = nn.Sequential(
+            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
+            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
+            decoder_layer(num_channels, num_channels, scale_factor=2, mode=mode),
+            decoder_layer(num_channels, in_channels, size=(h, w), mode=mode)
+        )
+
+        self.pos_encoder = PositionalEncoding(in_channels, dropout=0, max_len=max_length)
+        self.project = nn.Linear(in_channels, in_channels)
+
+    def forward(self, x):
+        N, E, H, W = x.size()
+        k, v = x, x  # (N, E, H, W)
+
+        # calculate key vector
+        features = []
+        for i in range(0, len(self.k_encoder)):
+            k = self.k_encoder[i](k)
+            features.append(k)
+        for i in range(0, len(self.k_decoder) - 1):
+            k = self.k_decoder[i](k)
+            k = k + features[len(self.k_decoder) - 2 - i]
+        k = self.k_decoder[-1](k)
+
+        # calculate query vector
+        # TODO q=f(q,k)
+        zeros = x.new_zeros((self.max_length, N, E))  # (T, N, E)
+        q = self.pos_encoder(zeros)  # (T, N, E)
+        q = q.permute(1, 0, 2)  # (N, T, E)
+        q = self.project(q)  # (N, T, E)
+        
+        # calculate attention
+        attn_scores = torch.bmm(q, k.flatten(2, 3))  # (N, T, (H*W))
+        attn_scores = attn_scores / (E ** 0.5)
+        attn_scores = torch.softmax(attn_scores, dim=-1)
+
+        v = v.permute(0, 2, 3, 1).view(N, -1, E)  # (N, (H*W), E)
+        attn_vecs = torch.bmm(attn_scores, v)  # (N, T, E)
+
+        return attn_vecs, attn_scores.view(N, -1, H, W)

modules/backbone.py

@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+from fastai.vision import *
+
+from modules.model import _default_tfmer_cfg
+from modules.resnet import resnet45
+from modules.transformer import (PositionalEncoding,
+                                 TransformerEncoder,
+                                 TransformerEncoderLayer)
+
+
+class ResTranformer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.resnet = resnet45()
+
+        self.d_model = ifnone(config.model_vision_d_model, _default_tfmer_cfg['d_model'])
+        nhead = ifnone(config.model_vision_nhead, _default_tfmer_cfg['nhead'])
+        d_inner = ifnone(config.model_vision_d_inner, _default_tfmer_cfg['d_inner'])
+        dropout = ifnone(config.model_vision_dropout, _default_tfmer_cfg['dropout'])
+        activation = ifnone(config.model_vision_activation, _default_tfmer_cfg['activation'])
+        num_layers = ifnone(config.model_vision_backbone_ln, 2)
+
+        self.pos_encoder = PositionalEncoding(self.d_model, max_len=8*32)
+        encoder_layer = TransformerEncoderLayer(d_model=self.d_model, nhead=nhead, 
+                dim_feedforward=d_inner, dropout=dropout, activation=activation)
+        self.transformer = TransformerEncoder(encoder_layer, num_layers)
+
+    def forward(self, images):
+        feature = self.resnet(images)
+        n, c, h, w = feature.shape
+        feature = feature.view(n, c, -1).permute(2, 0, 1)
+        feature = self.pos_encoder(feature)
+        feature = self.transformer(feature)
+        feature = feature.permute(1, 2, 0).view(n, c, h, w)
+        return feature

modules/model.py

@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+
+from utils import CharsetMapper
+
+
+_default_tfmer_cfg = dict(d_model=512, nhead=8, d_inner=2048, # 1024
+                          dropout=0.1, activation='relu')
+
+class Model(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.max_length = config.dataset_max_length + 1
+        self.charset = CharsetMapper(config.dataset_charset_path, max_length=self.max_length)
+    
+    def load(self, source, device=None, strict=True):
+        state = torch.load(source, map_location=device)
+        self.load_state_dict(state['model'], strict=strict)
+
+    def _get_length(self, logit, dim=-1):
+        """ Greed decoder to obtain length from logit"""
+        out = (logit.argmax(dim=-1) == self.charset.null_label)
+        abn = out.any(dim)
+        out = ((out.cumsum(dim) == 1) & out).max(dim)[1]
+        out = out + 1  # additional end token
+        out = torch.where(abn, out, out.new_tensor(logit.shape[1]))
+        return out
+
+    @staticmethod
+    def _get_padding_mask(length, max_length):
+        length = length.unsqueeze(-1)
+        grid = torch.arange(0, max_length, device=length.device).unsqueeze(0)
+        return grid >= length
+
+    @staticmethod
+    def _get_square_subsequent_mask(sz, device, diagonal=0, fw=True):
+        r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
+            Unmasked positions are filled with float(0.0).
+        """
+        mask = (torch.triu(torch.ones(sz, sz, device=device), diagonal=diagonal) == 1)
+        if fw: mask = mask.transpose(0, 1)
+        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
+        return mask
+
+    @staticmethod
+    def _get_location_mask(sz, device=None):
+        mask = torch.eye(sz, device=device)
+        mask = mask.float().masked_fill(mask == 1, float('-inf'))
+        return mask

modules/model_abinet.py

@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+from fastai.vision import *
+
+from .model_vision import BaseVision
+from .model_language import BCNLanguage
+from .model_alignment import BaseAlignment
+
+
+class ABINetModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.use_alignment = ifnone(config.model_use_alignment, True)
+        self.max_length = config.dataset_max_length + 1  # additional stop token
+        self.vision = BaseVision(config)
+        self.language = BCNLanguage(config)
+        if self.use_alignment: self.alignment = BaseAlignment(config)
+
+    def forward(self, images, *args):
+        v_res = self.vision(images)
+        v_tokens = torch.softmax(v_res['logits'], dim=-1)
+        v_lengths = v_res['pt_lengths'].clamp_(2, self.max_length)  # TODO:move to langauge model
+
+        l_res = self.language(v_tokens, v_lengths)
+        if not self.use_alignment:
+            return l_res, v_res
+        l_feature, v_feature = l_res['feature'], v_res['feature']
+        
+        a_res = self.alignment(l_feature, v_feature)
+        return a_res, l_res, v_res

modules/model_abinet_iter.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from fastai.vision import *
+
+from .model_vision import BaseVision
+from .model_language import BCNLanguage
+from .model_alignment import BaseAlignment
+
+
+class ABINetIterModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.iter_size = ifnone(config.model_iter_size, 1)
+        self.max_length = config.dataset_max_length + 1  # additional stop token
+        self.vision = BaseVision(config)
+        self.language = BCNLanguage(config)
+        self.alignment = BaseAlignment(config)
+
+    def forward(self, images, *args):
+        v_res = self.vision(images)
+        a_res = v_res
+        all_l_res, all_a_res = [], []
+        for _ in range(self.iter_size):
+            tokens = torch.softmax(a_res['logits'], dim=-1)
+            lengths = a_res['pt_lengths']
+            lengths.clamp_(2, self.max_length)  # TODO:move to langauge model
+            l_res = self.language(tokens, lengths)
+            all_l_res.append(l_res)
+            a_res = self.alignment(l_res['feature'], v_res['feature'])
+            all_a_res.append(a_res)
+        if self.training:
+            return all_a_res, all_l_res, v_res
+        else:
+            return a_res, all_l_res[-1], v_res

modules/model_alignment.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+from fastai.vision import *
+
+from modules.model import Model, _default_tfmer_cfg
+
+
+class BaseAlignment(Model):
+    def __init__(self, config):
+        super().__init__(config)
+        d_model = ifnone(config.model_alignment_d_model, _default_tfmer_cfg['d_model'])
+
+        self.loss_weight = ifnone(config.model_alignment_loss_weight, 1.0)
+        self.max_length = config.dataset_max_length + 1  # additional stop token
+        self.w_att = nn.Linear(2 * d_model, d_model)
+        self.cls = nn.Linear(d_model, self.charset.num_classes)
+
+    def forward(self, l_feature, v_feature):
+        """
+        Args:
+            l_feature: (N, T, E) where T is length, N is batch size and d is dim of model
+            v_feature: (N, T, E) shape the same as l_feature 
+            l_lengths: (N,)
+            v_lengths: (N,)
+        """
+        f = torch.cat((l_feature, v_feature), dim=2)
+        f_att = torch.sigmoid(self.w_att(f))
+        output = f_att * v_feature + (1 - f_att) * l_feature
+
+        logits = self.cls(output)  # (N, T, C)
+        pt_lengths = self._get_length(logits)
+
+        return {'logits': logits, 'pt_lengths': pt_lengths, 'loss_weight':self.loss_weight,
+                'name': 'alignment'}

modules/model_language.py

@@ -0,0 +1,67 @@
+import logging
+import torch.nn as nn
+from fastai.vision import *
+
+from modules.model import _default_tfmer_cfg
+from modules.model import Model
+from modules.transformer import (PositionalEncoding, 
+                                 TransformerDecoder,
+                                 TransformerDecoderLayer)
+
+
+class BCNLanguage(Model):
+    def __init__(self, config):
+        super().__init__(config)
+        d_model = ifnone(config.model_language_d_model, _default_tfmer_cfg['d_model'])
+        nhead = ifnone(config.model_language_nhead, _default_tfmer_cfg['nhead'])
+        d_inner = ifnone(config.model_language_d_inner, _default_tfmer_cfg['d_inner'])
+        dropout = ifnone(config.model_language_dropout, _default_tfmer_cfg['dropout'])
+        activation = ifnone(config.model_language_activation, _default_tfmer_cfg['activation'])
+        num_layers = ifnone(config.model_language_num_layers, 4)
+        self.d_model = d_model
+        self.detach = ifnone(config.model_language_detach, True)
+        self.use_self_attn = ifnone(config.model_language_use_self_attn, False)
+        self.loss_weight = ifnone(config.model_language_loss_weight, 1.0)
+        self.max_length = config.dataset_max_length + 1  # additional stop token
+        self.debug = ifnone(config.global_debug, False)
+
+        self.proj = nn.Linear(self.charset.num_classes, d_model, False)
+        self.token_encoder = PositionalEncoding(d_model, max_len=self.max_length)
+        self.pos_encoder = PositionalEncoding(d_model, dropout=0, max_len=self.max_length)
+        decoder_layer = TransformerDecoderLayer(d_model, nhead, d_inner, dropout, 
+                activation, self_attn=self.use_self_attn, debug=self.debug)
+        self.model = TransformerDecoder(decoder_layer, num_layers)
+
+        self.cls = nn.Linear(d_model, self.charset.num_classes)
+
+        if config.model_language_checkpoint is not None:
+            logging.info(f'Read language model from {config.model_language_checkpoint}.')
+            self.load(config.model_language_checkpoint)
+
+    def forward(self, tokens, lengths):
+        """
+        Args:
+            tokens: (N, T, C) where T is length, N is batch size and C is classes number
+            lengths: (N,)
+        """
+        if self.detach: tokens = tokens.detach()
+        embed = self.proj(tokens)  # (N, T, E)
+        embed = embed.permute(1, 0, 2)  # (T, N, E)
+        embed = self.token_encoder(embed)  # (T, N, E)
+        padding_mask = self._get_padding_mask(lengths, self.max_length)
+
+        zeros = embed.new_zeros(*embed.shape)
+        qeury = self.pos_encoder(zeros)
+        location_mask = self._get_location_mask(self.max_length, tokens.device)
+        output = self.model(qeury, embed,
+                tgt_key_padding_mask=padding_mask,
+                memory_mask=location_mask,
+                memory_key_padding_mask=padding_mask)  # (T, N, E)
+        output = output.permute(1, 0, 2)  # (N, T, E)
+
+        logits = self.cls(output)  # (N, T, C)
+        pt_lengths = self._get_length(logits)
+
+        res =  {'feature': output, 'logits': logits, 'pt_lengths': pt_lengths,
+                'loss_weight':self.loss_weight, 'name': 'language'}
+        return res

modules/model_vision.py

@@ -0,0 +1,47 @@
+import logging
+import torch.nn as nn
+from fastai.vision import *
+
+from modules.attention import *
+from modules.backbone import ResTranformer
+from modules.model import Model
+from modules.resnet import resnet45
+
+
+class BaseVision(Model):
+    def __init__(self, config):
+        super().__init__(config)
+        self.loss_weight = ifnone(config.model_vision_loss_weight, 1.0)
+        self.out_channels = ifnone(config.model_vision_d_model, 512)
+
+        if config.model_vision_backbone == 'transformer':
+            self.backbone = ResTranformer(config)
+        else: self.backbone = resnet45()
+        
+        if config.model_vision_attention == 'position':
+            mode = ifnone(config.model_vision_attention_mode, 'nearest')
+            self.attention = PositionAttention(
+                max_length=config.dataset_max_length + 1,  # additional stop token
+                mode=mode,
+            )
+        elif config.model_vision_attention == 'attention':
+            self.attention = Attention(
+                max_length=config.dataset_max_length + 1,  # additional stop token
+                n_feature=8*32,
+            )
+        else:
+            raise Exception(f'{config.model_vision_attention} is not valid.')
+        self.cls = nn.Linear(self.out_channels, self.charset.num_classes)
+
+        if config.model_vision_checkpoint is not None:
+            logging.info(f'Read vision model from {config.model_vision_checkpoint}.')
+            self.load(config.model_vision_checkpoint)
+
+    def forward(self, images, *args):
+        features = self.backbone(images)  # (N, E, H, W)
+        attn_vecs, attn_scores = self.attention(features)  # (N, T, E), (N, T, H, W)
+        logits = self.cls(attn_vecs) # (N, T, C)
+        pt_lengths = self._get_length(logits)
+
+        return {'feature': attn_vecs, 'logits': logits, 'pt_lengths': pt_lengths,
+                'attn_scores': attn_scores, 'loss_weight':self.loss_weight, 'name': 'vision'}

modules/resnet.py

@@ -0,0 +1,104 @@
+import math
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv1x1(inplanes, planes)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers):
+        self.inplanes = 32
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.layer1 = self._make_layer(block, 32, layers[0], stride=2)
+        self.layer2 = self._make_layer(block, 64, layers[1], stride=1)
+        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 256, layers[3], stride=1)
+        self.layer5 = self._make_layer(block, 512, layers[4], stride=1)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.layer5(x)
+        return x
+
+
+def resnet45():
+    return ResNet(BasicBlock, [3, 4, 6, 6, 3])

modules/transformer.py

@@ -0,0 +1,901 @@
+# pytorch 1.5.0
+import copy
+import math
+import warnings
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import Dropout, LayerNorm, Linear, Module, ModuleList, Parameter
+from torch.nn import functional as F
+from torch.nn.init import constant_, xavier_uniform_
+
+
+def multi_head_attention_forward(query,                           # type: Tensor
+                                 key,                             # type: Tensor
+                                 value,                           # type: Tensor
+                                 embed_dim_to_check,              # type: int
+                                 num_heads,                       # type: int
+                                 in_proj_weight,                  # type: Tensor
+                                 in_proj_bias,                    # type: Tensor
+                                 bias_k,                          # type: Optional[Tensor]
+                                 bias_v,                          # type: Optional[Tensor]
+                                 add_zero_attn,                   # type: bool
+                                 dropout_p,                       # type: float
+                                 out_proj_weight,                 # type: Tensor
+                                 out_proj_bias,                   # type: Tensor
+                                 training=True,                   # type: bool
+                                 key_padding_mask=None,           # type: Optional[Tensor]
+                                 need_weights=True,               # type: bool
+                                 attn_mask=None,                  # type: Optional[Tensor]
+                                 use_separate_proj_weight=False,  # type: bool
+                                 q_proj_weight=None,              # type: Optional[Tensor]
+                                 k_proj_weight=None,              # type: Optional[Tensor]
+                                 v_proj_weight=None,              # type: Optional[Tensor]
+                                 static_k=None,                   # type: Optional[Tensor]
+                                 static_v=None                    # type: Optional[Tensor]
+                                 ):
+    # type: (...) -> Tuple[Tensor, Optional[Tensor]]
+    r"""
+    Args:
+        query, key, value: map a query and a set of key-value pairs to an output.
+            See "Attention Is All You Need" for more details.
+        embed_dim_to_check: total dimension of the model.
+        num_heads: parallel attention heads.
+        in_proj_weight, in_proj_bias: input projection weight and bias.
+        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        dropout_p: probability of an element to be zeroed.
+        out_proj_weight, out_proj_bias: the output projection weight and bias.
+        training: apply dropout if is ``True``.
+        key_padding_mask: if provided, specified padding elements in the key will
+            be ignored by the attention. This is an binary mask. When the value is True,
+            the corresponding value on the attention layer will be filled with -inf.
+        need_weights: output attn_output_weights.
+        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+        use_separate_proj_weight: the function accept the proj. weights for query, key,
+            and value in different forms. If false, in_proj_weight will be used, which is
+            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
+        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
+        static_k, static_v: static key and value used for attention operators.
+    Shape:
+        Inputs:
+        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
+          If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions
+          will be unchanged. If a BoolTensor is provided, the positions with the
+          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+          S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
+          positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+          while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
+          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+          is provided, it will be added to the attention weight.
+        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
+          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
+        Outputs:
+        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+          E is the embedding dimension.
+        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
+          L is the target sequence length, S is the source sequence length.
+    """
+    # if not torch.jit.is_scripting():
+    #     tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v,
+    #                 out_proj_weight, out_proj_bias)
+    #     if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):
+    #         return handle_torch_function(
+    #             multi_head_attention_forward, tens_ops, query, key, value,
+    #             embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias,
+    #             bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight,
+    #             out_proj_bias, training=training, key_padding_mask=key_padding_mask,
+    #             need_weights=need_weights, attn_mask=attn_mask,
+    #             use_separate_proj_weight=use_separate_proj_weight,
+    #             q_proj_weight=q_proj_weight, k_proj_weight=k_proj_weight,
+    #             v_proj_weight=v_proj_weight, static_k=static_k, static_v=static_v)
+    tgt_len, bsz, embed_dim = query.size()
+    assert embed_dim == embed_dim_to_check
+    assert key.size() == value.size()
+
+    head_dim = embed_dim // num_heads
+    assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"
+    scaling = float(head_dim) ** -0.5
+
+    if not use_separate_proj_weight:
+        if torch.equal(query, key) and torch.equal(key, value):
+            # self-attention
+            q, k, v = F.linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)
+
+        elif torch.equal(key, value):
+            # encoder-decoder attention
+            # This is inline in_proj function with in_proj_weight and in_proj_bias
+            _b = in_proj_bias
+            _start = 0
+            _end = embed_dim
+            _w = in_proj_weight[_start:_end, :]
+            if _b is not None:
+                _b = _b[_start:_end]
+            q = F.linear(query, _w, _b)
+
+            if key is None:
+                assert value is None
+                k = None
+                v = None
+            else:
+
+                # This is inline in_proj function with in_proj_weight and in_proj_bias
+                _b = in_proj_bias
+                _start = embed_dim
+                _end = None
+                _w = in_proj_weight[_start:, :]
+                if _b is not None:
+                    _b = _b[_start:]
+                k, v = F.linear(key, _w, _b).chunk(2, dim=-1)
+
+        else:
+            # This is inline in_proj function with in_proj_weight and in_proj_bias
+            _b = in_proj_bias
+            _start = 0
+            _end = embed_dim
+            _w = in_proj_weight[_start:_end, :]
+            if _b is not None:
+                _b = _b[_start:_end]
+            q = F.linear(query, _w, _b)
+
+            # This is inline in_proj function with in_proj_weight and in_proj_bias
+            _b = in_proj_bias
+            _start = embed_dim
+            _end = embed_dim * 2
+            _w = in_proj_weight[_start:_end, :]
+            if _b is not None:
+                _b = _b[_start:_end]
+            k = F.linear(key, _w, _b)
+
+            # This is inline in_proj function with in_proj_weight and in_proj_bias
+            _b = in_proj_bias
+            _start = embed_dim * 2
+            _end = None
+            _w = in_proj_weight[_start:, :]
+            if _b is not None:
+                _b = _b[_start:]
+            v = F.linear(value, _w, _b)
+    else:
+        q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)
+        len1, len2 = q_proj_weight_non_opt.size()
+        assert len1 == embed_dim and len2 == query.size(-1)
+
+        k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)
+        len1, len2 = k_proj_weight_non_opt.size()
+        assert len1 == embed_dim and len2 == key.size(-1)
+
+        v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)
+        len1, len2 = v_proj_weight_non_opt.size()
+        assert len1 == embed_dim and len2 == value.size(-1)
+
+        if in_proj_bias is not None:
+            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])
+            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)])
+            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):])
+        else:
+            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias)
+            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias)
+            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias)
+    q = q * scaling
+
+    if attn_mask is not None:
+        assert attn_mask.dtype == torch.float32 or attn_mask.dtype == torch.float64 or \
+            attn_mask.dtype == torch.float16 or attn_mask.dtype == torch.uint8 or attn_mask.dtype == torch.bool, \
+            'Only float, byte, and bool types are supported for attn_mask, not {}'.format(attn_mask.dtype)
+        if attn_mask.dtype == torch.uint8:
+            warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
+            attn_mask = attn_mask.to(torch.bool)
+
+        if attn_mask.dim() == 2:
+            attn_mask = attn_mask.unsqueeze(0)
+            if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
+                raise RuntimeError('The size of the 2D attn_mask is not correct.')
+        elif attn_mask.dim() == 3:
+            if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]:
+                raise RuntimeError('The size of the 3D attn_mask is not correct.')
+        else:
+            raise RuntimeError("attn_mask's dimension {} is not supported".format(attn_mask.dim()))
+        # attn_mask's dim is 3 now.
+
+    # # convert ByteTensor key_padding_mask to bool
+    # if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+    #     warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
+    #     key_padding_mask = key_padding_mask.to(torch.bool)
+
+    if bias_k is not None and bias_v is not None:
+        if static_k is None and static_v is None:
+            k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = pad(attn_mask, (0, 1))
+            if key_padding_mask is not None:
+                key_padding_mask = pad(key_padding_mask, (0, 1))
+        else:
+            assert static_k is None, "bias cannot be added to static key."
+            assert static_v is None, "bias cannot be added to static value."
+    else:
+        assert bias_k is None
+        assert bias_v is None
+
+    q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
+    if k is not None:
+        k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
+    if v is not None:
+        v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
+
+    if static_k is not None:
+        assert static_k.size(0) == bsz * num_heads
+        assert static_k.size(2) == head_dim
+        k = static_k
+
+    if static_v is not None:
+        assert static_v.size(0) == bsz * num_heads
+        assert static_v.size(2) == head_dim
+        v = static_v
+
+    src_len = k.size(1)
+
+    if key_padding_mask is not None:
+        assert key_padding_mask.size(0) == bsz
+        assert key_padding_mask.size(1) == src_len
+
+    if add_zero_attn:
+        src_len += 1
+        k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)
+        v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)
+        if attn_mask is not None:
+            attn_mask = pad(attn_mask, (0, 1))
+        if key_padding_mask is not None:
+            key_padding_mask = pad(key_padding_mask, (0, 1))
+
+    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+    assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]
+
+    if attn_mask is not None:
+        if attn_mask.dtype == torch.bool:
+            attn_output_weights.masked_fill_(attn_mask, float('-inf'))
+        else:
+            attn_output_weights += attn_mask
+
+
+    if key_padding_mask is not None:
+        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
+        attn_output_weights = attn_output_weights.masked_fill(
+            key_padding_mask.unsqueeze(1).unsqueeze(2),
+            float('-inf'),
+        )
+        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)
+
+    attn_output_weights = F.softmax(
+        attn_output_weights, dim=-1)
+    attn_output_weights = F.dropout(attn_output_weights, p=dropout_p, training=training)
+
+    attn_output = torch.bmm(attn_output_weights, v)
+    assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
+    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+    attn_output = F.linear(attn_output, out_proj_weight, out_proj_bias)
+
+    if need_weights:
+        # average attention weights over heads
+        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
+        return attn_output, attn_output_weights.sum(dim=1) / num_heads
+    else:
+        return attn_output, None
+
+class MultiheadAttention(Module):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces.
+    See reference: Attention Is All You Need
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+    Args:
+        embed_dim: total dimension of the model.
+        num_heads: parallel attention heads.
+        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
+        bias: add bias as module parameter. Default: True.
+        add_bias_kv: add bias to the key and value sequences at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        kdim: total number of features in key. Default: None.
+        vdim: total number of features in value. Default: None.
+        Note: if kdim and vdim are None, they will be set to embed_dim such that
+        query, key, and value have the same number of features.
+    Examples::
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+    """
+    # __annotations__ = {
+    #     'bias_k': torch._jit_internal.Optional[torch.Tensor],
+    #     'bias_v': torch._jit_internal.Optional[torch.Tensor],
+    # }
+    __constants__ = ['q_proj_weight', 'k_proj_weight', 'v_proj_weight', 'in_proj_weight']
+
+    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):
+        super(MultiheadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+
+        if self._qkv_same_embed_dim is False:
+            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
+            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
+            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
+            self.register_parameter('in_proj_weight', None)
+        else:
+            self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))
+            self.register_parameter('q_proj_weight', None)
+            self.register_parameter('k_proj_weight', None)
+            self.register_parameter('v_proj_weight', None)
+
+        if bias:
+            self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
+        else:
+            self.register_parameter('in_proj_bias', None)
+        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.empty(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.empty(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        if self._qkv_same_embed_dim:
+            xavier_uniform_(self.in_proj_weight)
+        else:
+            xavier_uniform_(self.q_proj_weight)
+            xavier_uniform_(self.k_proj_weight)
+            xavier_uniform_(self.v_proj_weight)
+
+        if self.in_proj_bias is not None:
+            constant_(self.in_proj_bias, 0.)
+            constant_(self.out_proj.bias, 0.)
+        if self.bias_k is not None:
+            xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            xavier_normal_(self.bias_v)
+
+    def __setstate__(self, state):
+        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
+        if '_qkv_same_embed_dim' not in state:
+            state['_qkv_same_embed_dim'] = True
+
+        super(MultiheadAttention, self).__setstate__(state)
+
+    def forward(self, query, key, value, key_padding_mask=None,
+                need_weights=True, attn_mask=None):
+        # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
+        r"""
+    Args:
+        query, key, value: map a query and a set of key-value pairs to an output.
+            See "Attention Is All You Need" for more details.
+        key_padding_mask: if provided, specified padding elements in the key will
+            be ignored by the attention. This is an binary mask. When the value is True,
+            the corresponding value on the attention layer will be filled with -inf.
+        need_weights: output attn_output_weights.
+        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+    Shape:
+        - Inputs:
+        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+          the embedding dimension.
+        - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
+          If a ByteTensor is provided, the non-zero positions will be ignored while the position
+          with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
+          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+          S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+          positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+          while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
+          is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+          is provided, it will be added to the attention weight.
+        - Outputs:
+        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+          E is the embedding dimension.
+        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
+          L is the target sequence length, S is the source sequence length.
+        """
+        if not self._qkv_same_embed_dim:
+            return multi_head_attention_forward(
+                query, key, value, self.embed_dim, self.num_heads,
+                self.in_proj_weight, self.in_proj_bias,
+                self.bias_k, self.bias_v, self.add_zero_attn,
+                self.dropout, self.out_proj.weight, self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask, need_weights=need_weights,
+                attn_mask=attn_mask, use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
+                v_proj_weight=self.v_proj_weight)
+        else:
+            return multi_head_attention_forward(
+                query, key, value, self.embed_dim, self.num_heads,
+                self.in_proj_weight, self.in_proj_bias,
+                self.bias_k, self.bias_v, self.add_zero_attn,
+                self.dropout, self.out_proj.weight, self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask, need_weights=need_weights,
+                attn_mask=attn_mask)
+
+
+class Transformer(Module):
+    r"""A transformer model. User is able to modify the attributes as needed. The architecture
+    is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer,
+    Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and
+    Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information
+    Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805)
+    model with corresponding parameters.
+
+    Args:
+        d_model: the number of expected features in the encoder/decoder inputs (default=512).
+        nhead: the number of heads in the multiheadattention models (default=8).
+        num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6).
+        num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+        activation: the activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).
+        custom_encoder: custom encoder (default=None).
+        custom_decoder: custom decoder (default=None).
+
+    Examples::
+        >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
+        >>> src = torch.rand((10, 32, 512))
+        >>> tgt = torch.rand((20, 32, 512))
+        >>> out = transformer_model(src, tgt)
+
+    Note: A full example to apply nn.Transformer module for the word language model is available in
+    https://github.com/pytorch/examples/tree/master/word_language_model
+    """
+
+    def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
+                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", custom_encoder=None, custom_decoder=None):
+        super(Transformer, self).__init__()
+
+        if custom_encoder is not None:
+            self.encoder = custom_encoder
+        else:
+            encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation)
+            encoder_norm = LayerNorm(d_model)
+            self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+        if custom_decoder is not None:
+            self.decoder = custom_decoder
+        else:
+            decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation)
+            decoder_norm = LayerNorm(d_model)
+            self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)
+
+        self._reset_parameters()
+
+        self.d_model = d_model
+        self.nhead = nhead
+
+    def forward(self, src, tgt, src_mask=None, tgt_mask=None,
+                memory_mask=None, src_key_padding_mask=None,
+                tgt_key_padding_mask=None, memory_key_padding_mask=None):
+        # type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -> Tensor  # noqa
+        r"""Take in and process masked source/target sequences.
+
+        Args:
+            src: the sequence to the encoder (required).
+            tgt: the sequence to the decoder (required).
+            src_mask: the additive mask for the src sequence (optional).
+            tgt_mask: the additive mask for the tgt sequence (optional).
+            memory_mask: the additive mask for the encoder output (optional).
+            src_key_padding_mask: the ByteTensor mask for src keys per batch (optional).
+            tgt_key_padding_mask: the ByteTensor mask for tgt keys per batch (optional).
+            memory_key_padding_mask: the ByteTensor mask for memory keys per batch (optional).
+
+        Shape:
+            - src: :math:`(S, N, E)`.
+            - tgt: :math:`(T, N, E)`.
+            - src_mask: :math:`(S, S)`.
+            - tgt_mask: :math:`(T, T)`.
+            - memory_mask: :math:`(T, S)`.
+            - src_key_padding_mask: :math:`(N, S)`.
+            - tgt_key_padding_mask: :math:`(N, T)`.
+            - memory_key_padding_mask: :math:`(N, S)`.
+
+            Note: [src/tgt/memory]_mask ensures that position i is allowed to attend the unmasked
+            positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+            while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
+            are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+            is provided, it will be added to the attention weight. 
+            [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by
+            the attention. If a ByteTensor is provided, the non-zero positions will be ignored while the zero
+            positions will be unchanged. If a BoolTensor is provided, the positions with the
+            value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+
+            - output: :math:`(T, N, E)`.
+
+            Note: Due to the multi-head attention architecture in the transformer model,
+            the output sequence length of a transformer is same as the input sequence
+            (i.e. target) length of the decode.
+
+            where S is the source sequence length, T is the target sequence length, N is the
+            batch size, E is the feature number
+
+        Examples:
+            >>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
+        """
+
+        if src.size(1) != tgt.size(1):
+            raise RuntimeError("the batch number of src and tgt must be equal")
+
+        if src.size(2) != self.d_model or tgt.size(2) != self.d_model:
+            raise RuntimeError("the feature number of src and tgt must be equal to d_model")
+
+        memory = self.encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
+        output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask,
+                              tgt_key_padding_mask=tgt_key_padding_mask,
+                              memory_key_padding_mask=memory_key_padding_mask)
+        return output
+
+    def generate_square_subsequent_mask(self, sz):
+        r"""Generate a square mask for the sequence. The masked positions are filled with float('-inf').
+            Unmasked positions are filled with float(0.0).
+        """
+        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
+        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
+        return mask
+
+    def _reset_parameters(self):
+        r"""Initiate parameters in the transformer model."""
+
+        for p in self.parameters():
+            if p.dim() > 1:
+                xavier_uniform_(p)
+
+
+class TransformerEncoder(Module):
+    r"""TransformerEncoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+
+    Examples::
+        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
+        >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)
+        >>> src = torch.rand(10, 32, 512)
+        >>> out = transformer_encoder(src)
+    """
+    __constants__ = ['norm']
+
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super(TransformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(self, src, mask=None, src_key_padding_mask=None):
+        # type: (Tensor, Optional[Tensor], Optional[Tensor]) -> Tensor
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = src
+
+        for i, mod in enumerate(self.layers):
+            output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+
+class TransformerDecoder(Module):
+    r"""TransformerDecoder is a stack of N decoder layers
+
+    Args:
+        decoder_layer: an instance of the TransformerDecoderLayer() class (required).
+        num_layers: the number of sub-decoder-layers in the decoder (required).
+        norm: the layer normalization component (optional).
+
+    Examples::
+        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
+        >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
+        >>> memory = torch.rand(10, 32, 512)
+        >>> tgt = torch.rand(20, 32, 512)
+        >>> out = transformer_decoder(tgt, memory)
+    """
+    __constants__ = ['norm']
+
+    def __init__(self, decoder_layer, num_layers, norm=None):
+        super(TransformerDecoder, self).__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(self, tgt, memory, memory2=None, tgt_mask=None,
+                memory_mask=None, memory_mask2=None, tgt_key_padding_mask=None,
+                memory_key_padding_mask=None, memory_key_padding_mask2=None):
+        # type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -> Tensor
+        r"""Pass the inputs (and mask) through the decoder layer in turn.
+
+        Args:
+            tgt: the sequence to the decoder (required).
+            memory: the sequence from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        output = tgt
+
+        for mod in self.layers:
+            output = mod(output, memory, memory2=memory2, tgt_mask=tgt_mask,
+                         memory_mask=memory_mask, memory_mask2=memory_mask2,
+                         tgt_key_padding_mask=tgt_key_padding_mask,
+                         memory_key_padding_mask=memory_key_padding_mask,
+                         memory_key_padding_mask2=memory_key_padding_mask2)
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+class TransformerEncoderLayer(Module):
+    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
+    This standard encoder layer is based on the paper "Attention Is All You Need".
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
+    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
+    in a different way during application.
+
+    Args:
+        d_model: the number of expected features in the input (required).
+        nhead: the number of heads in the multiheadattention models (required).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+        activation: the activation function of intermediate layer, relu or gelu (default=relu).
+
+    Examples::
+        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
+        >>> src = torch.rand(10, 32, 512)
+        >>> out = encoder_layer(src)
+    """
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, 
+                 activation="relu", debug=False):
+        super(TransformerEncoderLayer, self).__init__()
+        self.debug = debug
+        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = Linear(d_model, dim_feedforward)
+        self.dropout = Dropout(dropout)
+        self.linear2 = Linear(dim_feedforward, d_model)
+
+        self.norm1 = LayerNorm(d_model)
+        self.norm2 = LayerNorm(d_model)
+        self.dropout1 = Dropout(dropout)
+        self.dropout2 = Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+
+    def __setstate__(self, state):
+        if 'activation' not in state:
+            state['activation'] = F.relu
+        super(TransformerEncoderLayer, self).__setstate__(state)
+
+    def forward(self, src, src_mask=None, src_key_padding_mask=None):
+        # type: (Tensor, Optional[Tensor], Optional[Tensor]) -> Tensor
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        src2, attn = self.self_attn(src, src, src, attn_mask=src_mask,
+                              key_padding_mask=src_key_padding_mask)
+        if self.debug: self.attn = attn
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        
+        return src
+
+
+class TransformerDecoderLayer(Module):
+    r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.
+    This standard decoder layer is based on the paper "Attention Is All You Need".
+    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
+    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
+    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
+    in a different way during application.
+
+    Args:
+        d_model: the number of expected features in the input (required).
+        nhead: the number of heads in the multiheadattention models (required).
+        dim_feedforward: the dimension of the feedforward network model (default=2048).
+        dropout: the dropout value (default=0.1).
+        activation: the activation function of intermediate layer, relu or gelu (default=relu).
+
+    Examples::
+        >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
+        >>> memory = torch.rand(10, 32, 512)
+        >>> tgt = torch.rand(20, 32, 512)
+        >>> out = decoder_layer(tgt, memory)
+    """
+
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, 
+                 activation="relu", self_attn=True, siamese=False, debug=False):
+        super(TransformerDecoderLayer, self).__init__()
+        self.has_self_attn, self.siamese = self_attn, siamese
+        self.debug = debug
+        if self.has_self_attn:
+            self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
+            self.norm1 = LayerNorm(d_model)
+            self.dropout1 = Dropout(dropout)
+        self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = Linear(d_model, dim_feedforward)
+        self.dropout = Dropout(dropout)
+        self.linear2 = Linear(dim_feedforward, d_model)
+
+        self.norm2 = LayerNorm(d_model)
+        self.norm3 = LayerNorm(d_model)
+        self.dropout2 = Dropout(dropout)
+        self.dropout3 = Dropout(dropout)
+        if self.siamese:
+            self.multihead_attn2 = MultiheadAttention(d_model, nhead, dropout=dropout)
+
+        self.activation = _get_activation_fn(activation)
+
+    def __setstate__(self, state):
+        if 'activation' not in state:
+            state['activation'] = F.relu
+        super(TransformerDecoderLayer, self).__setstate__(state)
+
+    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
+                tgt_key_padding_mask=None, memory_key_padding_mask=None,
+                memory2=None, memory_mask2=None, memory_key_padding_mask2=None):
+        # type: (Tensor, Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], Optional[Tensor]) -> Tensor
+        r"""Pass the inputs (and mask) through the decoder layer.
+
+        Args:
+            tgt: the sequence to the decoder layer (required).
+            memory: the sequence from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        if self.has_self_attn:
+            tgt2, attn = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,
+                                key_padding_mask=tgt_key_padding_mask)
+            tgt = tgt + self.dropout1(tgt2)
+            tgt = self.norm1(tgt)
+            if self.debug: self.attn = attn
+        tgt2, attn2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask,
+                                   key_padding_mask=memory_key_padding_mask)
+        if self.debug: self.attn2 = attn2
+
+        if self.siamese:
+            tgt3, attn3 = self.multihead_attn2(tgt, memory2, memory2, attn_mask=memory_mask2,
+                            key_padding_mask=memory_key_padding_mask2)
+            tgt = tgt + self.dropout2(tgt3)
+            if self.debug: self.attn3 = attn3
+
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        
+        return tgt
+
+
+def _get_clones(module, N):
+    return ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation):
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return F.gelu
+
+    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
+
+
+class PositionalEncoding(nn.Module):
+    r"""Inject some information about the relative or absolute position of the tokens
+        in the sequence. The positional encodings have the same dimension as
+        the embeddings, so that the two can be summed. Here, we use sine and cosine
+        functions of different frequencies.
+    .. math::
+        \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
+        \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
+        \text{where pos is the word position and i is the embed idx)
+    Args:
+        d_model: the embed dim (required).
+        dropout: the dropout value (default=0.1).
+        max_len: the max. length of the incoming sequence (default=5000).
+    Examples:
+        >>> pos_encoder = PositionalEncoding(d_model)
+    """
+
+    def __init__(self, d_model, dropout=0.1, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout)
+
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0).transpose(0, 1)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        r"""Inputs of forward function
+        Args:
+            x: the sequence fed to the positional encoder model (required).
+        Shape:
+            x: [sequence length, batch size, embed dim]
+            output: [sequence length, batch size, embed dim]
+        Examples:
+            >>> output = pos_encoder(x)
+        """
+
+        x = x + self.pe[:x.size(0), :]
+        return self.dropout(x)
+
+
+if __name__ == '__main__':
+    transformer_model = Transformer(nhead=16, num_encoder_layers=12)
+    src = torch.rand((10, 32, 512))
+    tgt = torch.rand((20, 32, 512))
+    out = transformer_model(src, tgt)
+    print(out)

requirements.txt

@@ -1,11 +1,17 @@
+torch==1.4.0
+torchvision==0.5.0
+fastai==1.0.60
+LMDB
+Pillow
+opencv-python
+tensorboardX
+PyYAML
+gdown
 ninja
 yacs
 cython
 matplotlib
 tqdm
-opencv-python
-torch==1.4.0
-torchvision==0.5.0
 shapely
 scipy
 networkx

utils.py

@@ -0,0 +1,304 @@
+import logging
+import os
+import time
+
+import cv2
+import numpy as np
+import torch
+import yaml
+from matplotlib import colors
+from matplotlib import pyplot as plt
+from torch import Tensor, nn
+from torch.utils.data import ConcatDataset
+
+class CharsetMapper(object):
+    """A simple class to map ids into strings.
+
+    It works only when the character set is 1:1 mapping between individual
+    characters and individual ids.
+    """
+
+    def __init__(self,
+                 filename='',
+                 max_length=30,
+                 null_char=u'\u2591'):
+        """Creates a lookup table.
+
+        Args:
+          filename: Path to charset file which maps characters to ids.
+          max_sequence_length: The max length of ids and string.
+          null_char: A unicode character used to replace '<null>' character.
+            the default value is a light shade block '░'.
+        """
+        self.null_char = null_char
+        self.max_length = max_length
+
+        self.label_to_char = self._read_charset(filename)
+        self.char_to_label = dict(map(reversed, self.label_to_char.items()))
+        self.num_classes = len(self.label_to_char)
+ 
+    def _read_charset(self, filename):
+        """Reads a charset definition from a tab separated text file.
+
+        Args:
+          filename: a path to the charset file.
+
+        Returns:
+          a dictionary with keys equal to character codes and values - unicode
+          characters.
+        """
+        import re
+        pattern = re.compile(r'(\d+)\t(.+)')
+        charset = {}
+        self.null_label = 0
+        charset[self.null_label] = self.null_char
+        with open(filename, 'r') as f:
+            for i, line in enumerate(f):
+                m = pattern.match(line)
+                assert m, f'Incorrect charset file. line #{i}: {line}'
+                label = int(m.group(1)) + 1
+                char = m.group(2)
+                charset[label] = char
+        return charset
+
+    def trim(self, text):
+        assert isinstance(text, str)
+        return text.replace(self.null_char, '')
+
+    def get_text(self, labels, length=None, padding=True, trim=False):
+        """ Returns a string corresponding to a sequence of character ids.
+        """
+        length = length if length else self.max_length
+        labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
+        if padding:
+            labels = labels + [self.null_label] * (length-len(labels))
+        text = ''.join([self.label_to_char[label] for label in labels])
+        if trim: text = self.trim(text)
+        return text
+
+    def get_labels(self, text, length=None, padding=True, case_sensitive=False):
+        """ Returns the labels of the corresponding text.
+        """
+        length = length if length else self.max_length
+        if padding:
+            text = text + self.null_char * (length - len(text))
+        if not case_sensitive:
+            text = text.lower()
+        labels = [self.char_to_label[char] for char in text]
+        return labels
+
+    def pad_labels(self, labels, length=None):
+        length = length if length else self.max_length
+
+        return labels + [self.null_label] * (length - len(labels))
+
+    @property
+    def digits(self):
+        return '0123456789'
+
+    @property
+    def digit_labels(self):
+        return self.get_labels(self.digits, padding=False)
+
+    @property
+    def alphabets(self):
+        all_chars = list(self.char_to_label.keys())
+        valid_chars = []
+        for c in all_chars:
+            if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
+                valid_chars.append(c)
+        return ''.join(valid_chars)
+
+    @property
+    def alphabet_labels(self):
+        return self.get_labels(self.alphabets, padding=False)
+
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.data_time = 0.
+        self.data_diff = 0.
+        self.data_total_time = 0.
+        self.data_call = 0
+        self.running_time = 0.
+        self.running_diff = 0.
+        self.running_total_time = 0.
+        self.running_call = 0
+
+    def tic(self):
+        self.start_time = time.time()
+        self.running_time = self.start_time
+
+    def toc_data(self):
+        self.data_time = time.time()
+        self.data_diff = self.data_time - self.running_time
+        self.data_total_time += self.data_diff
+        self.data_call += 1
+
+    def toc_running(self):
+        self.running_time = time.time()
+        self.running_diff = self.running_time - self.data_time
+        self.running_total_time += self.running_diff
+        self.running_call += 1
+
+    def total_time(self):
+        return self.data_total_time + self.running_total_time
+
+    def average_time(self):
+        return  self.average_data_time() + self.average_running_time()
+
+    def average_data_time(self):
+        return self.data_total_time / (self.data_call or 1)
+
+    def average_running_time(self):
+        return self.running_total_time / (self.running_call or 1)
+
+
+class Logger(object):
+    _handle = None
+    _root = None
+
+    @staticmethod
+    def init(output_dir, name, phase):
+        format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
+                '%(message)s'.format(name)
+        logging.basicConfig(level=logging.INFO, format=format)
+
+        try: os.makedirs(output_dir)
+        except: pass
+        config_path = os.path.join(output_dir, f'{phase}.txt')
+        Logger._handle = logging.FileHandler(config_path)
+        Logger._root = logging.getLogger()
+
+    @staticmethod
+    def enable_file():
+        if Logger._handle is None or Logger._root is None:
+            raise Exception('Invoke Logger.init() first!')
+        Logger._root.addHandler(Logger._handle)
+
+    @staticmethod
+    def disable_file():
+        if Logger._handle is None or Logger._root is None:
+            raise Exception('Invoke Logger.init() first!')
+        Logger._root.removeHandler(Logger._handle)
+
+
+class Config(object):
+
+    def __init__(self, config_path, host=True):
+        def __dict2attr(d, prefix=''):
+            for k, v in d.items():
+                if isinstance(v, dict):
+                    __dict2attr(v, f'{prefix}{k}_')
+                else:
+                    if k == 'phase':
+                        assert v in ['train', 'test']
+                    if k == 'stage':
+                        assert v in ['pretrain-vision', 'pretrain-language',
+                                     'train-semi-super', 'train-super']
+                    self.__setattr__(f'{prefix}{k}', v)
+
+        assert os.path.exists(config_path), '%s does not exists!' % config_path
+        with open(config_path) as file:
+            config_dict = yaml.load(file, Loader=yaml.FullLoader)
+        with open('configs/rec/template.yaml') as file:
+            default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
+        __dict2attr(default_config_dict)
+        __dict2attr(config_dict)
+        self.global_workdir = os.path.join(self.global_workdir, self.global_name)
+
+    def __getattr__(self, item):
+        attr = self.__dict__.get(item)
+        if attr is None:
+            attr = dict()
+            prefix = f'{item}_'
+            for k, v in self.__dict__.items():
+                if k.startswith(prefix):
+                    n = k.replace(prefix, '')
+                    attr[n] = v
+            return attr if len(attr) > 0 else None
+        else:
+            return attr
+
+    def __repr__(self):
+        str = 'ModelConfig(\n'
+        for i, (k, v) in enumerate(sorted(vars(self).items())):
+            str += f'\t({i}): {k} = {v}\n'
+        str += ')'
+        return str
+
+def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
+    # normalize mask
+    mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
+    if mask.shape != image.shape:
+        mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
+    # get color map
+    color_map = plt.get_cmap(cmap)
+    mask = color_map(mask)[:,:,:3]
+    # convert float to uint8
+    mask = (mask * 255).astype(dtype=np.uint8)
+
+    # set the basic color
+    basic_color = np.array(colors.to_rgb(color)) * 255 
+    basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1]) 
+    basic_color = basic_color.astype(dtype=np.uint8)
+    # blend with basic color
+    blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
+    # blend with mask
+    blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
+
+    return blended_img
+
+def onehot(label, depth, device=None):
+    """ 
+    Args:
+        label: shape (n1, n2, ..., )
+        depth: a scalar
+
+    Returns:
+        onehot: (n1, n2, ..., depth)
+    """
+    if not isinstance(label, torch.Tensor):
+        label = torch.tensor(label, device=device)
+    onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
+    onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
+
+    return onehot
+
+class MyDataParallel(nn.DataParallel):
+
+    def gather(self, outputs, target_device):
+        r"""
+        Gathers tensors from different GPUs on a specified device
+        (-1 means the CPU).
+        """
+        def gather_map(outputs):
+            out = outputs[0]
+            if isinstance(out, (str, int, float)):
+                return out
+            if isinstance(out, list) and isinstance(out[0], str):
+                return [o for out in outputs for o in out]
+            if isinstance(out, torch.Tensor):
+                return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
+            if out is None:
+                return None
+            if isinstance(out, dict):
+                if not all((len(out) == len(d) for d in outputs)):
+                    raise ValueError('All dicts must have the same number of keys')
+                return type(out)(((k, gather_map([d[k] for d in outputs]))
+                                for k in out))
+            return type(out)(map(gather_map, zip(*outputs)))
+
+        # Recursive function calls like this create reference cycles.
+        # Setting the function to None clears the refcycle.
+        try:
+            res = gather_map(outputs)
+        finally:
+            gather_map = None
+        return res
+
+
+class MyConcatDataset(ConcatDataset):
+    def __getattr__(self, k): 
+        return getattr(self.datasets[0], k)