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import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import time
import os
from collections import defaultdict
import json
import captioning.utils.opts as opts
import captioning.models as models
from captioning.data.pth_loader import CaptionDataset
import captioning.utils.eval_utils as eval_utils
# import captioning.utils.vizwiz_eval_utils as vizwiz_eval_utils
import captioning.utils.misc as utils
from captioning.utils.rewards import init_scorer, get_self_critical_reward
from captioning.modules.loss_wrapper import LossWrapper
import pytorch_lightning as pl
class ModelCheckpoint(pl.callbacks.ModelCheckpoint):
def on_keyboard_interrupt(self, trainer, pl_module):
# Save model when keyboard interrupt
filepath = os.path.join(self.dirpath, self.prefix + 'interrupt.ckpt')
self._save_model(filepath)
if __name__ == '__main__':
device = 'cuda'
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--reward', type=str, default='mle')
args = parser.parse_args()
if args.reward == 'mle':
cfg = f'configs/phase1/fg_clipRN50_{args.reward}.yml'
else:
cfg = f'configs/phase2/fg_clipRN50_{args.reward}.yml'
print("Loading cfg from", cfg)
opt = opts.parse_opt(parse=False, cfg=cfg)
dataset = CaptionDataset(opt)
opt.vocab_size = dataset.vocab_size
opt.seq_length = dataset.seq_length
opt.batch_size = 40
opt.vocab = dataset.get_vocab()
model = models.setup(opt)
del opt.vocab
ckpt_path = opt.checkpoint_path + '-last.ckpt'
print("Loading checkpoint from", ckpt_path)
raw_state_dict = torch.load(
ckpt_path,
map_location=device)
strict = True
state_dict = raw_state_dict['state_dict']
if '_vocab' in state_dict:
model.vocab = utils.deserialize(state_dict['_vocab'])
del state_dict['_vocab']
elif strict:
raise KeyError
if '_opt' in state_dict:
saved_model_opt = utils.deserialize(state_dict['_opt'])
del state_dict['_opt']
# Make sure the saved opt is compatible with the curren topt
need_be_same = ["caption_model",
"rnn_type", "rnn_size", "num_layers"]
for checkme in need_be_same:
if getattr(saved_model_opt, checkme) in ['updown', 'topdown'] and \
getattr(opt, checkme) in ['updown', 'topdown']:
continue
assert getattr(saved_model_opt, checkme) == getattr(
opt, checkme), "Command line argument and saved model disagree on '%s' " % checkme
elif strict:
raise KeyError
res = model.load_state_dict(state_dict, strict)
print(res)
opt.use_grammar = False
lw_model = LossWrapper(model, opt)
split = 'test'
print("Building dataloader...")
test_dataset = torch.utils.data.Subset(
dataset,
dataset.split_ix[split]
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=4,
drop_last=False,
collate_fn=dataset.collate_func
)
eval_kwargs = {'dataset': opt.input_json}
eval_kwargs.update(vars(opt))
verbose = eval_kwargs.get('verbose', True)
verbose_beam = eval_kwargs.get('verbose_beam', 0)
verbose_loss = eval_kwargs.get('verbose_loss', 1)
# num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
# lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
sample_n = eval_kwargs.get('sample_n', 1)
remove_bad_endings = eval_kwargs.get('remove_bad_endings', 0)
crit = lw_model.crit
model = model.to(device)
from tqdm import tqdm
test_id2sent = {}
model.eval()
print("running inference...")
for data in tqdm(test_loader):
with torch.no_grad():
# forward the model to get loss
tmp = [data['fc_feats'], data['att_feats'],
data['labels'], data['masks'], data['att_masks']]
tmp = [d.to(device) if isinstance(d, torch.Tensor) else d for d in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
loss = crit(model(fc_feats, att_feats,
labels[..., :-1], att_masks), labels[..., 1:], masks[..., 1:])
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
tmp_eval_kwargs = eval_kwargs.copy()
tmp_eval_kwargs.update({'sample_n': 1})
seq, seq_logprobs = model(
fc_feats, att_feats, att_masks, opt=tmp_eval_kwargs, mode='sample')
seq = seq.data
entropy = - (F.softmax(seq_logprobs, dim=2) *
seq_logprobs).sum(2).sum(1) / ((seq > 0).to(seq_logprobs).sum(1)+1)
perplexity = - \
seq_logprobs.gather(2, seq.unsqueeze(2)).squeeze(
2).sum(1) / ((seq > 0).to(seq_logprobs).sum(1)+1)
# Print beam search
if beam_size > 1 and verbose_beam:
for i in range(fc_feats.shape[0]):
print('\n'.join([utils.decode_sequence(model.vocab, _[
'seq'].unsqueeze(0))[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(model.vocab, seq)
for d, sent in zip(data['infos'], sents):
test_id2sent[d['id']] = sent
res_path = f'FineCapEval_results/clipRN50_{args.reward}.json'
print("Results save at {}".format(res_path))
with open(res_path, 'w') as f:
json.dump(test_id2sent, f)
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