code stringlengths 17 6.64M |
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class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 ... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.C... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
... |
def resnet18(pretrained=False):
'Constructs a ResNet-18 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [2, 2, 2, 2])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model... |
def resnet34(pretrained=False):
'Constructs a ResNet-34 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model... |
def resnet50(pretrained=False):
'Constructs a ResNet-50 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model... |
def resnet101(pretrained=False):
'Constructs a ResNet-101 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 23, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return m... |
def resnet152(pretrained=False):
'Constructs a ResNet-152 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 8, 36, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return m... |
class myResnet(nn.Module):
def __init__(self, resnet):
super(myResnet, self).__init__()
self.resnet = resnet
def forward(self, img, att_size=14):
x = img.unsqueeze(0)
x = self.resnet.conv1(x)
x = self.resnet.bn1(x)
x = self.resnet.relu(x)
x = self.resn... |
def init_scorer(cached_tokens):
global CiderD_scorer
CiderD_scorer = (CiderD_scorer or CiderD(df=cached_tokens))
|
def array_to_str(arr):
out = ''
for i in range(len(arr)):
out += (str(arr[i]) + ' ')
if (arr[i] == 0):
break
return out.strip()
|
def get_self_critical_reward(data, gen_result, greedy_res):
batch_size = gen_result.size(0)
seq_per_img = (batch_size // len(data['gts']))
res = OrderedDict()
gen_result = gen_result.data.cpu().numpy()
greedy_res = greedy_res.data.cpu().numpy()
for i in range(batch_size):
res[i] = [arr... |
def setup(opt, model_name, caption=True):
if caption:
if (model_name == 'show_tell'):
model = ShowTellModel(opt)
elif (model_name == 'show_attend_tell'):
model = ShowAttendTellModel(opt)
elif (model_name == 'all_img'):
model = AllImgModel(opt)
el... |
def load(model, opt):
if (vars(opt).get('start_from', None) is not None):
assert os.path.isdir(opt.start_from), (' %s must be a a path' % opt.start_from)
assert os.path.isfile(os.path.join(opt.start_from, (('infos_' + opt.id) + '.pkl'))), ('infos.pkl file does not exist in path %s' % opt.start_fro... |
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--input_json', type=str, default='data/coco.json', help='path to the json file containing additional info and vocab')
parser.add_argument('--input_fc_dir', type=str, default='data/cocotalk_fc', help='path to the directory containing ... |
def build_vocab(imgs, params):
count_thr = params['word_count_threshold']
counts = {}
for img in imgs:
for sent in img['sentences']:
for w in sent['tokens']:
counts[w] = (counts.get(w, 0) + 1)
cw = sorted([(count, w) for (w, count) in counts.items()], reverse=True)
... |
def encode_captions(imgs, params, wtoi):
' \n encode all captions into one large array, which will be 1-indexed.\n also produces label_start_ix and label_end_ix which store 1-indexed \n and inclusive (Lua-style) pointers to the first and last caption for\n each image in the dataset.\n '
max_length = para... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
seed(123)
vocab = build_vocab(imgs, params)
itow = {(i + 1): w for (i, w) in enumerate(vocab)}
wtoi = {w: (i + 1) for (i, w) in enumerate(vocab)}
(L, label_start_ix, label_end_ix, label_length) = enco... |
def precook(s, n=4, out=False):
'\n Takes a string as input and returns an object that can be given to\n either cook_refs or cook_test. This is optional: cook_refs and cook_test\n can take string arguments as well.\n :param s: string : sentence to be converted into ngrams\n :param n: int : number of ngram... |
def cook_refs(refs, n=4):
'Takes a list of reference sentences for a single segment\n and returns an object that encapsulates everything that BLEU\n needs to know about them.\n :param refs: list of string : reference sentences for some image\n :param n: int : number of ngrams for which (ngram) represe... |
def create_crefs(refs):
crefs = []
for ref in refs:
crefs.append(cook_refs(ref))
return crefs
|
def compute_doc_freq(crefs):
'\n Compute term frequency for reference data.\n This will be used to compute idf (inverse document frequency later)\n The term frequency is stored in the object\n :return: None\n '
document_frequency = defaultdict(float)
for refs in crefs:
for ngram in set([ngram... |
def build_dict(imgs, wtoi, params):
wtoi['<eos>'] = 0
count_imgs = 0
refs_words = []
refs_idxs = []
for img in imgs:
if ((params['split'] == img['split']) or ((params['split'] == 'train') and (img['split'] == 'restval')) or (params['split'] == 'all')):
ref_words = []
... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
itow = json.load(open(params['dict_json'], 'r'))['ix_to_word']
wtoi = {w: i for (i, w) in itow.items()}
imgs = imgs['images']
(ngram_words, ngram_idxs, ref_len) = build_dict(imgs, wtoi, params)
cPickle.dump({'document_frequenc... |
def train(opt):
opt.use_att = utils.if_use_att(opt)
loader = DataLoader(opt)
opt.vocab_size = loader.vocab_size
opt.seq_length = loader.seq_length
tf_summary_writer = (tf and SummaryWriter(opt.checkpoint_path))
infos = {}
histories = {}
if (opt.start_from is not None):
with ope... |
def find_ngrams(input_list, n):
return zip(*[input_list[i:] for i in range(n)])
|
def compute_div_n(caps, n=1):
aggr_div = []
for k in caps:
all_ngrams = set()
lenT = 0.0
for c in caps[k]:
tkns = c.split()
lenT += len(tkns)
ng = find_ngrams(tkns, n)
all_ngrams.update(ng)
aggr_div.append((float(len(all_ngrams)) ... |
def compute_global_div_n(caps, n=1):
aggr_div = []
all_ngrams = set()
lenT = 0.0
for k in caps:
for c in caps[k]:
tkns = c.split()
lenT += len(tkns)
ng = find_ngrams(tkns, n)
all_ngrams.update(ng)
if (n == 1):
aggr_div.append(float(le... |
class ResNet(torchvision.models.resnet.ResNet):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__(block, layers, num_classes)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)
for i in range(2, 5):
getattr(self, ('l... |
def resnet18(pretrained=False):
'Constructs a ResNet-18 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [2, 2, 2, 2])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model... |
def resnet34(pretrained=False):
'Constructs a ResNet-34 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model... |
def resnet50(pretrained=False):
'Constructs a ResNet-50 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model... |
def resnet101(pretrained=False):
'Constructs a ResNet-101 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 23, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return m... |
def resnet152(pretrained=False):
'Constructs a ResNet-152 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 8, 36, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return m... |
class myResnet(nn.Module):
def __init__(self, resnet):
super(myResnet, self).__init__()
self.resnet = resnet
def forward(self, img, att_size=14):
x = img.unsqueeze(0)
x = self.resnet.conv1(x)
x = self.resnet.bn1(x)
x = self.resnet.relu(x)
x = self.resn... |
def setup(opt):
if (opt.caption_model == 'fc'):
model = FCModel(opt)
elif (opt.caption_model == 'language_model'):
model = LMModel(opt)
elif (opt.caption_model == 'newfc'):
model = NewFCModel(opt)
elif (opt.caption_model == 'show_tell'):
model = ShowTellModel(opt)
e... |
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--input_json', type=str, default='data/coco.json', help='path to the json file containing additional info and vocab')
parser.add_argument('--input_fc_dir', type=str, default='data/cocotalk_fc', help='path to the directory containing ... |
def add_vse_options(parser):
parser.add_argument('--initialize_retrieval', type=str, default=None, help='xxxx.pth')
parser.add_argument('--vse_model', type=str, default='None', help='fc, None')
parser.add_argument('--vse_rnn_type', type=str, default='gru', help='rnn, gru, or lstm')
parser.add_argument... |
def add_eval_options(parser):
parser.add_argument('--batch_size', type=int, default=0, help='if > 0 then overrule, otherwise load from checkpoint.')
parser.add_argument('--num_images', type=int, default=(- 1), help='how many images to use when periodically evaluating the loss? (-1 = all)')
parser.add_argu... |
def add_diversity_opts(parser):
parser.add_argument('--sample_n', type=int, default=1, help='Diverse sampling')
parser.add_argument('--sample_n_method', type=str, default='sample', help='sample, bs, dbs, gumbel, topk')
parser.add_argument('--eval_oracle', type=int, default=1, help='if we need to calculate... |
def build_vocab(imgs, params):
count_thr = params['word_count_threshold']
counts = {}
for img in imgs:
for sent in img['sentences']:
for w in sent['tokens']:
counts[w] = (counts.get(w, 0) + 1)
cw = sorted([(count, w) for (w, count) in counts.items()], reverse=True)
... |
def encode_captions(imgs, params, wtoi):
' \n encode all captions into one large array, which will be 1-indexed.\n also produces label_start_ix and label_end_ix which store 1-indexed \n and inclusive (Lua-style) pointers to the first and last caption for\n each image in the dataset.\n '
max_length = para... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
seed(123)
vocab = build_vocab(imgs, params)
itow = {(i + 1): w for (i, w) in enumerate(vocab)}
wtoi = {w: (i + 1) for (i, w) in enumerate(vocab)}
(L, label_start_ix, label_end_ix, label_length) = enco... |
def precook(s, n=4, out=False):
'\n Takes a string as input and returns an object that can be given to\n either cook_refs or cook_test. This is optional: cook_refs and cook_test\n can take string arguments as well.\n :param s: string : sentence to be converted into ngrams\n :param n: int : number of ngram... |
def cook_refs(refs, n=4):
'Takes a list of reference sentences for a single segment\n and returns an object that encapsulates everything that BLEU\n needs to know about them.\n :param refs: list of string : reference sentences for some image\n :param n: int : number of ngrams for which (ngram) represe... |
def create_crefs(refs):
crefs = []
for ref in refs:
crefs.append(cook_refs(ref))
return crefs
|
def compute_doc_freq(crefs):
'\n Compute term frequency for reference data.\n This will be used to compute idf (inverse document frequency later)\n The term frequency is stored in the object\n :return: None\n '
document_frequency = defaultdict(float)
for refs in crefs:
for ngram in set([ngram... |
def build_dict(imgs, wtoi, params):
wtoi['<eos>'] = 0
count_imgs = 0
refs_words = []
refs_idxs = []
for img in imgs:
if ((params['split'] == img['split']) or ((params['split'] == 'train') and (img['split'] == 'restval')) or (params['split'] == 'all')):
ref_words = []
... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
itow = json.load(open(params['dict_json'], 'r'))['ix_to_word']
wtoi = {w: i for (i, w) in itow.items()}
imgs = imgs['images']
(ngram_words, ngram_idxs, ref_len) = build_dict(imgs, wtoi, params)
utils.pickle_dump({'document_fre... |
class MultiHeadedDotAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1, scale=1, project_k_v=1, use_output_layer=1, do_aoa=0, norm_q=0, dropout_aoa=0.3):
super(MultiHeadedDotAttention, self).__init__()
assert (((d_model * scale) % h) == 0)
self.d_k = ((d_model * scale) // h)... |
class AoA_Refiner_Layer(nn.Module):
def __init__(self, size, self_attn, feed_forward, dropout):
super(AoA_Refiner_Layer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.use_ff = 0
if (self.feed_forward is not None):
self.use_ff... |
class AoA_Refiner_Core(nn.Module):
def __init__(self, opt):
super(AoA_Refiner_Core, self).__init__()
attn = MultiHeadedDotAttention(opt.num_heads, opt.rnn_size, project_k_v=1, scale=opt.multi_head_scale, do_aoa=opt.refine_aoa, norm_q=0, dropout_aoa=getattr(opt, 'dropout_aoa', 0.3))
layer ... |
class AoA_Decoder_Core(nn.Module):
def __init__(self, opt):
super(AoA_Decoder_Core, self).__init__()
self.drop_prob_lm = opt.drop_prob_lm
self.d_model = opt.rnn_size
self.use_multi_head = opt.use_multi_head
self.multi_head_scale = opt.multi_head_scale
self.use_ctx_... |
class AoAModel(AttModel):
def __init__(self, opt):
super(AoAModel, self).__init__(opt)
self.num_layers = 2
self.use_mean_feats = getattr(opt, 'mean_feats', 1)
if (opt.use_multi_head == 2):
del self.ctx2att
self.ctx2att = nn.Linear(opt.rnn_size, ((2 * opt.mu... |
def setup(opt):
if (opt.caption_model in ['fc', 'show_tell']):
print(('Warning: %s model is mostly deprecated; many new features are not supported.' % opt.caption_model))
if (opt.caption_model == 'fc'):
print('Use newfc instead of fc')
if (opt.caption_model == 'fc'):
model ... |
def repeat_tensors(n, x):
'\n For a tensor of size Bx..., we repeat it n times, and make it Bnx...\n For collections, do nested repeat\n '
if torch.is_tensor(x):
x = x.unsqueeze(1)
x = x.expand((- 1), n, *([(- 1)] * len(x.shape[2:])))
x = x.reshape((x.shape[0] * n), *x.shape[2... |
def split_tensors(n, x):
if torch.is_tensor(x):
assert ((x.shape[0] % n) == 0)
x = x.reshape((x.shape[0] // n), n, *x.shape[1:]).unbind(1)
elif ((type(x) is list) or (type(x) is tuple)):
x = [split_tensors(n, _) for _ in x]
elif (x is None):
x = ([None] * n)
return x
|
class CfgNode(_CfgNode):
'\n Our own extended version of :class:`yacs.config.CfgNode`.\n It contains the following extra features:\n\n 1. The :meth:`merge_from_file` method supports the "_BASE_" key,\n which allows the new CfgNode to inherit all the attributes from the\n base configuration fi... |
def find_ngrams(input_list, n):
return zip(*[input_list[i:] for i in range(n)])
|
def compute_div_n(caps, n=1):
aggr_div = []
for k in caps:
all_ngrams = set()
lenT = 0.0
for c in caps[k]:
tkns = c.split()
lenT += len(tkns)
ng = find_ngrams(tkns, n)
all_ngrams.update(ng)
aggr_div.append((float(len(all_ngrams)) ... |
def compute_global_div_n(caps, n=1):
aggr_div = []
all_ngrams = set()
lenT = 0.0
for k in caps:
for c in caps[k]:
tkns = c.split()
lenT += len(tkns)
ng = find_ngrams(tkns, n)
all_ngrams.update(ng)
if (n == 1):
aggr_div.append(float(le... |
def pickle_load(f):
' Load a pickle.\n Parameters\n ----------\n f: file-like object\n '
if six.PY3:
return cPickle.load(f, encoding='latin-1')
else:
return cPickle.load(f)
|
def pickle_dump(obj, f):
' Dump a pickle.\n Parameters\n ----------\n obj: pickled object\n f: file-like object\n '
if six.PY3:
return cPickle.dump(obj, f, protocol=2)
else:
return cPickle.dump(obj, f)
|
def serialize_to_tensor(data):
device = torch.device('cpu')
buffer = cPickle.dumps(data)
storage = torch.ByteStorage.from_buffer(buffer)
tensor = torch.ByteTensor(storage).to(device=device)
return tensor
|
def deserialize(tensor):
buffer = tensor.cpu().numpy().tobytes()
return cPickle.loads(buffer)
|
def decode_sequence(ix_to_word, seq):
(N, D) = seq.size()
out = []
for i in range(N):
txt = ''
for j in range(D):
ix = seq[(i, j)]
if (ix > 0):
if (j >= 1):
txt = (txt + ' ')
txt = (txt + ix_to_word[str(ix.item())]... |
def save_checkpoint(opt, model, infos, optimizer, histories=None, append=''):
if (len(append) > 0):
append = ('-' + append)
if (not os.path.isdir(opt.checkpoint_path)):
os.makedirs(opt.checkpoint_path)
checkpoint_path = os.path.join(opt.checkpoint_path, ('model%s.pth' % append))
torch.... |
def set_lr(optimizer, lr):
for group in optimizer.param_groups:
group['lr'] = lr
|
def get_lr(optimizer):
for group in optimizer.param_groups:
return group['lr']
|
def build_optimizer(params, opt):
if (opt.optim == 'rmsprop'):
return optim.RMSprop(params, opt.learning_rate, opt.optim_alpha, opt.optim_epsilon, weight_decay=opt.weight_decay)
elif (opt.optim == 'adagrad'):
return optim.Adagrad(params, opt.learning_rate, weight_decay=opt.weight_decay)
el... |
def penalty_builder(penalty_config):
if (penalty_config == ''):
return (lambda x, y: y)
(pen_type, alpha) = penalty_config.split('_')
alpha = float(alpha)
if (pen_type == 'wu'):
return (lambda x, y: length_wu(x, y, alpha))
if (pen_type == 'avg'):
return (lambda x, y: length... |
def length_wu(length, logprobs, alpha=0.0):
'\n NMT length re-ranking score from\n "Google\'s Neural Machine Translation System" :cite:`wu2016google`.\n '
modifier = (((5 + length) ** alpha) / ((5 + 1) ** alpha))
return (logprobs / modifier)
|
def length_average(length, logprobs, alpha=0.0):
'\n Returns the average probability of tokens in a sequence.\n '
return (logprobs / length)
|
class NoamOpt(torch.optim.Optimizer):
'Optim wrapper that implements rate.'
def __init__(self, model_size, factor, warmup, optimizer):
self.optimizer = optimizer
self._step = 0
self.warmup = warmup
self.factor = factor
self.model_size = model_size
self._rate = ... |
class ReduceLROnPlateau(torch.optim.Optimizer):
'Optim wrapper that implements rate.'
def __init__(self, optimizer, mode='min', factor=0.1, patience=10, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08, verbose=False):
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(optim... |
def get_std_opt(model, optim_func='adam', factor=1, warmup=2000):
optim_func = dict(adam=torch.optim.Adam, adamw=torch.optim.AdamW)[optim_func]
return NoamOpt(model.d_model, factor, warmup, optim_func(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-09))
|
def if_use_feat(caption_model):
if (caption_model in ['show_tell', 'all_img', 'fc', 'newfc']):
(use_att, use_fc) = (False, True)
elif (caption_model == 'language_model'):
(use_att, use_fc) = (False, False)
elif (caption_model in ['updown', 'topdown']):
(use_fc, use_att) = (True, Tr... |
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--input_json', type=str, default='data/coco.json', help='path to the json file containing additional info and vocab')
parser.add_argument('--input_fc_dir', type=str, default='data/cocotalk_fc', help='path to the directory containing ... |
def add_eval_options(parser):
parser.add_argument('--batch_size', type=int, default=0, help='if > 0 then overrule, otherwise load from checkpoint.')
parser.add_argument('--num_images', type=int, default=(- 1), help='how many images to use when periodically evaluating the loss? (-1 = all)')
parser.add_argu... |
def add_diversity_opts(parser):
parser.add_argument('--sample_n', type=int, default=1, help='Diverse sampling')
parser.add_argument('--sample_n_method', type=str, default='sample', help='sample, bs, dbs, gumbel, topk, dgreedy, dsample, dtopk, dtopp')
parser.add_argument('--eval_oracle', type=int, default=... |
def add_eval_sample_opts(parser):
parser.add_argument('--sample_method', type=str, default='greedy', help='greedy; sample; gumbel; top<int>, top<0-1>')
parser.add_argument('--beam_size', type=int, default=1, help='used when sample_method = greedy, indicates number of beams in beam search. Usually 2 or 3 works... |
class ResNet(torchvision.models.resnet.ResNet):
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__(block, layers, num_classes)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)
for i in range(2, 5):
getattr(self, ('l... |
def resnet18(pretrained=False):
'Constructs a ResNet-18 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [2, 2, 2, 2])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model... |
def resnet34(pretrained=False):
'Constructs a ResNet-34 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(BasicBlock, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model... |
def resnet50(pretrained=False):
'Constructs a ResNet-50 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model... |
def resnet101(pretrained=False):
'Constructs a ResNet-101 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 4, 23, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return m... |
def resnet152(pretrained=False):
'Constructs a ResNet-152 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n '
model = ResNet(Bottleneck, [3, 8, 36, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return m... |
class myResnet(nn.Module):
def __init__(self, resnet):
super(myResnet, self).__init__()
self.resnet = resnet
def forward(self, img, att_size=14):
x = img.unsqueeze(0)
x = self.resnet.conv1(x)
x = self.resnet.bn1(x)
x = self.resnet.relu(x)
x = self.resn... |
def build_vocab(imgs, params):
captions = []
for img in imgs:
for sent in img['sentences']:
captions.append(' '.join(sent['tokens']))
captions = '\n'.join(captions)
all_captions = tempfile.NamedTemporaryFile(delete=False)
all_captions.close()
with open(all_captions.name, 'w... |
def encode_captions(imgs, params, wtoi):
' \n\tencode all captions into one large array, which will be 1-indexed.\n\talso produces label_start_ix and label_end_ix which store 1-indexed \n\tand inclusive (Lua-style) pointers to the first and last caption for\n\teach image in the dataset.\n\t'
max_length = para... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
seed(123)
(vocab, bpe) = build_vocab(imgs, params)
itow = {(i + 1): w for (i, w) in enumerate(vocab)}
wtoi = {w: (i + 1) for (i, w) in enumerate(vocab)}
(L, label_start_ix, label_end_ix, label_length)... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
if (params['fc_input_dir'] is not None):
print('processing fc')
with h5py.File(params['fc_output']) as file_fc:
for (i, img) in enumerate(tqdm(imgs)):
npy... |
class FolderLMDB(data.Dataset):
def __init__(self, db_path, fn_list=None):
self.db_path = db_path
self.lmdb = lmdbdict(db_path, unsafe=True)
self.lmdb._key_dumps = DUMPS_FUNC['ascii']
self.lmdb._value_loads = LOADS_FUNC['identity']
if (fn_list is not None):
sel... |
def make_dataset(dir, extension):
images = []
dir = os.path.expanduser(dir)
for (root, _, fnames) in sorted(os.walk(dir)):
for fname in sorted(fnames):
if has_file_allowed_extension(fname, [extension]):
path = os.path.join(root, fname)
images.append(path... |
def raw_reader(path):
with open(path, 'rb') as f:
bin_data = f.read()
return bin_data
|
def raw_npz_reader(path):
with open(path, 'rb') as f:
bin_data = f.read()
try:
npz_data = np.load(six.BytesIO(bin_data))['feat']
except Exception as e:
print(path)
npz_data = None
print(e)
return (bin_data, npz_data)
|
def raw_npy_reader(path):
with open(path, 'rb') as f:
bin_data = f.read()
try:
npy_data = np.load(six.BytesIO(bin_data))
except Exception as e:
print(path)
npy_data = None
print(e)
return (bin_data, npy_data)
|
class Folder(data.Dataset):
def __init__(self, root, loader, extension, fn_list=None):
super(Folder, self).__init__()
self.root = root
if fn_list:
samples = [os.path.join(root, (str(_) + extension)) for _ in fn_list]
else:
samples = make_dataset(self.root, ... |
def folder2lmdb(dpath, fn_list, write_frequency=5000):
directory = osp.expanduser(osp.join(dpath))
print(('Loading dataset from %s' % directory))
if (args.extension == '.npz'):
dataset = Folder(directory, loader=raw_npz_reader, extension='.npz', fn_list=fn_list)
else:
dataset = Folder(... |
def parse_args():
'\n Parse input arguments\n '
parser = argparse.ArgumentParser(description='Generate bbox output from a Fast R-CNN network')
parser.add_argument('--input_json', default='./data/dataset_coco.json', type=str)
parser.add_argument('--output_file', default='.dump_cache.tsv', type=st... |
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