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eval_utils_en_copy.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils
def language_eval(dataset, preds, model_id, split):
import sys
sys.path.append("coco-caption")
annFile='data/aic_process/ALL_11683_v3_COCOCN_val_test_en.json'
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
# encoder.FLOAT_REPR = lambda o: format(o, '.3f')
if not os.path.isdir('eval_results'):
os.mkdir('eval_results')
cache_path = os.path.join('eval_results/', model_id + '_' + split + '.json')
cache_path_1 = os.path.join('eval_results/', model_id + '_' + split +'_scores'+'.json')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt = [p for p in preds if p['image_id'] in valids]
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
json.dump(preds_filt, open(cache_path, 'w')) # serialize to temporary json file. Sigh, COCO API...
print("Dump to json file {}".format(cache_path))
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
with open(cache_path_1, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval}, outfile)
print("Dump to json file {}".format(cache_path_1))
return out, imgToEval
def eval_split(model, crit, loader, p_flag, eval_kwargs={}):
verbose = eval_kwargs.get('verbose', True)
verbose_beam = eval_kwargs.get('verbose_beam', 1)
verbose_loss = eval_kwargs.get('verbose_loss', 1)
num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
# num_images =150
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
output_cap_path = eval_kwargs.get('output_cap_path', '0')
rela_dict_path = 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path,allow_pickle=True)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
fc_feats = None
att_feats = None
att_masks = None
ssg_data = None
rela_data = None
# forward the model to get loss
if use_rela == 1:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks'],
data['rela_matrix'], data['rela_masks'], data['attr_matrix'], data['attr_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks, rela_matrix, rela_masks, attr_matrix, attr_masks = tmp
rela_data = {}
rela_data['att_feats'] = att_feats
rela_data['att_masks'] = att_masks
rela_data['rela_matrix'] = rela_matrix
rela_data['rela_masks'] = rela_masks
rela_data['attr_matrix'] = attr_matrix
rela_data['attr_masks'] = attr_masks
elif use_ssg == 1:
if p_flag == 1:
tmp = [data['fc_feats'], data['ssg_paired_rela_matrix'], data['ssg_paired_rela_masks'],
data['ssg_paired_obj'], data['ssg_paired_obj_masks'],
data['ssg_paired_attr'], data['ssg_paired_attr_masks'], data['labels_p'], data['masks_p']]
# print (tmp)
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'], data['ssg_rela_matrix'], data['ssg_rela_masks'], data['ssg_obj'],
data['ssg_obj_masks'],
data['ssg_attr'], data['ssg_attr_masks'], data['labels'], data['masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
with torch.no_grad():
# loss = crit(model(fc_feats, labels, rela_data, ssg_data), labels[:, 1:], masks[:, 1:]).item()
outputs, _, _, _ = model(fc_feats, labels, rela_data, ssg_data)
loss = crit(outputs, labels[:, 1:], masks[:, 1:]).item()
loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
fc_feats = None
att_feats = None
att_masks = None
isg_data = None
ssg_data = None
rela_data = None
if use_ssg == 1:
if p_flag== 0:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
else:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_paired_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels_p'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks_p'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, att_masks, labels, masks = tmp
# forward the model to also get generated samples for each image
with torch.no_grad():
seq = model(fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
#Print beam search
if p_flag==0:
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 1) == 1:
entry['file_name'] = data['infos'][k]['file_path']
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
else:
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab_p(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab_p(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab_p(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos_p'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 1) == 1:
entry['file_name'] = data['infos_p'][k]['file_path']
if eval_kwargs.get('dump_impages', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos_p'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
if p_flag==0:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap_' + eval_kwargs['id'] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
else:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id_p'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap_' + eval_kwargs['id_p'] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
# Switch back to training mode
model.train()
return loss_sum/loss_evals, predictions, lang_stats
def eval_split_nmt(model, crit, loader, eval_kwargs={}):
verbose = eval_kwargs.get('verbose', True)
verbose_beam = eval_kwargs.get('verbose_beam', 1)
verbose_loss = eval_kwargs.get('verbose_loss', 1)
num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
# num_images=150
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
output_cap_path = eval_kwargs.get('output_cap_path', '0')
rela_dict_path = 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path,allow_pickle=True)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
ssg_data = None
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
# forward the model to also get generated samples for each image
with torch.no_grad():
seq = model(fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
#Print beam search
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap_'+ eval_kwargs['id'] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
# Switch back to training mode
model.train()
return loss_sum/loss_evals, predictions, lang_stats
def eval_split_up(model, use_mapping, netG_obj, netG_rel, netG_atr, crit, loader, eval_kwargs={}):
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))
split = eval_kwargs.get('split', 'test')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
gan_type = eval_kwargs.get('gan_type', 0)
freeze_i2t = eval_kwargs.get('freeze_i2t', 0)
output_cap_path = eval_kwargs.get('output_cap_path', '0')
rela_dict_path = 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
if netG_rel is not None: netG_rel.eval()
if netG_obj is not None: netG_obj.eval()
if netG_atr is not None: netG_atr.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
fc_feats = None
att_feats = None
att_masks = None
ssg_data = None
rela_data = None
# forward the model to get loss
if use_rela == 1:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks'],
data['rela_matrix'], data['rela_masks'], data['attr_matrix'], data['attr_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks, rela_matrix, rela_masks, attr_matrix, attr_masks = tmp
rela_data = {}
rela_data['att_feats'] = att_feats
rela_data['att_masks'] = att_masks
rela_data['rela_matrix'] = rela_matrix
rela_data['rela_masks'] = rela_masks
rela_data['attr_matrix'] = attr_matrix
rela_data['attr_masks'] = attr_masks
elif use_ssg == 1:
tmp = [data['fc_feats'], data['ssg_rela_matrix'], data['ssg_rela_masks'], data['ssg_obj'],
data['ssg_obj_masks'],
data['ssg_attr'], data['ssg_attr_masks'], data['labels'], data['masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
#with torch.no_grad():
# loss = crit(model(fc_feats, att_feats, labels, att_masks, rela_data, ssg_data), labels[:,1:], masks[:,1:]).item()
# loss = crit(model(fc_feats, labels, rela_data, ssg_data), labels[:, 1:], masks[:, 1:]).item()
#loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
fc_feats = None
att_feats = None
att_masks = None
isg_data = None
ssg_data = None
rela_data = None
if use_ssg == 1:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, att_masks, labels, masks = tmp
# forward the model to also get generated samples for each image
with torch.no_grad():
if use_mapping:
if gan_type == 0:
seq = model(netG_obj, netG_rel, netG_atr, fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
elif gan_type == 1:
seq = model(netG_obj, netG_rel, None, fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
elif gan_type == 2:
seq = model(netG_obj, None, None, fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
else:
seq = model(None, None, None, fc_feats, ssg_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
#Print beam search
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap_'+ eval_kwargs['id'] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
# Switch back to training mode
if freeze_i2t == 0:
model.train()
if netG_rel is not None: netG_rel.train()
if netG_obj is not None: netG_obj.train()
if netG_atr is not None: netG_atr.train()
return loss_sum/loss_evals, predictions, lang_stats
def eval_split_isg(model, crit, loader, eval_kwargs={}):
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))
split = eval_kwargs.get('split', 'test')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
gan_type = eval_kwargs.get('gan_type', 0)
freeze_i2t = eval_kwargs.get('freeze_i2t', 0)
output_cap_path = eval_kwargs.get('output_cap_path', '0')
rela_dict_path = 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['isg_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, isg_rela_matrix, isg_rela_masks, isg_obj, isg_obj_masks, isg_attr, isg_attr_masks, labels, masks = tmp
isg_data = {}
isg_data['isg_rela_matrix'] = isg_rela_matrix
isg_data['isg_rela_masks'] = isg_rela_masks
isg_data['isg_obj'] = isg_obj
isg_data['isg_obj_masks'] = isg_obj_masks
isg_data['isg_attr'] = isg_attr
isg_data['isg_attr_masks'] = isg_attr_masks
# forward the model to also get generated samples for each image
with torch.no_grad():
seq = model(fc_feats, isg_data, opt=eval_kwargs, mode='sample')[0].data
#Print beam search
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap_'+ eval_kwargs['id'] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
# Switch back to training mode
model.train()
return loss_sum/loss_evals, predictions, lang_stats
def eval_split_raw(model, crit, loader, eval_kwargs={}):
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))
split = eval_kwargs.get('split', 'test')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
output_cap_path = eval_kwargs.get('output_cap_path', '0')
rela_dict_path = 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
fc_feats = None
att_feats = None
att_masks = None
ssg_data = None
rela_data = None
# forward the model to get loss
if use_rela == 1:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks'],
data['rela_matrix'], data['rela_masks'], data['attr_matrix'], data['attr_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks, rela_matrix, rela_masks, attr_matrix, attr_masks = tmp
rela_data = {}
rela_data['att_feats'] = att_feats
rela_data['att_masks'] = att_masks
rela_data['rela_matrix'] = rela_matrix
rela_data['rela_masks'] = rela_masks
rela_data['attr_matrix'] = attr_matrix
rela_data['attr_masks'] = attr_masks
elif use_ssg == 1:
tmp = [data['fc_feats'], data['ssg_rela_matrix'], data['ssg_rela_masks'], data['ssg_obj'],
data['ssg_obj_masks'],
data['ssg_attr'], data['ssg_attr_masks'], data['labels'], data['masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'], data['att_feats'], data['labels'], data['masks'], data['att_masks']]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, labels, masks, att_masks = tmp
with torch.no_grad():
loss = crit(model(fc_feats, att_feats, labels, att_masks, rela_data, ssg_data), labels[:,1:], masks[:,1:]).item()
loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
fc_feats = None
att_feats = None
att_masks = None
ssg_data = None
rela_data = None
if use_rela == 1:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]
]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, att_masks, rela_matrix, rela_masks, attr_matrix, attr_masks,\
labels, masks = tmp
rela_data = {}
rela_data['att_feats'] = att_feats
rela_data['att_masks'] = att_masks
rela_data['rela_matrix'] = rela_matrix
rela_data['rela_masks'] = rela_masks
rela_data['attr_matrix'] = attr_matrix
rela_data['attr_masks'] = attr_masks
elif use_ssg == 1:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_obj_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr'][np.arange(loader.batch_size) * loader.seq_per_img],
data['ssg_attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [_ if _ is None else torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, ssg_rela_matrix, ssg_rela_masks, ssg_obj, ssg_obj_masks, ssg_attr, ssg_attr_masks, labels, masks = tmp
ssg_data = {}
ssg_data['ssg_rela_matrix'] = ssg_rela_matrix
ssg_data['ssg_rela_masks'] = ssg_rela_masks
ssg_data['ssg_obj'] = ssg_obj
ssg_data['ssg_obj_masks'] = ssg_obj_masks
ssg_data['ssg_attr'] = ssg_attr
ssg_data['ssg_attr_masks'] = ssg_attr_masks
else:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, att_masks, labels, masks = tmp
# forward the model to also get generated samples for each image
with torch.no_grad():
seq = model(fc_feats, att_feats, att_masks, rela_data, ssg_data, opt=eval_kwargs, mode='sample')[0].data
#Print beam search
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0),
use_ssg=use_ssg)[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq, use_ssg=use_ssg)
gt_captions = utils.decode_sequence(loader.get_vocab(), labels[:,1:], use_ssg=use_ssg)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
entry['gt_caption'] = gt_captions[k]
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
predictions.append(entry)
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' %(ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
lang_stats, scores_each = language_eval(dataset, predictions, eval_kwargs['id'], split)
if verbose:
for img_id in scores_each.keys():
print('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
print('cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file = open('gen_cap/cap'+ eval_kwargs['model'][-10:-4] + '.txt', "aw")
text_file.write('image %s, %s' % (scores_each[img_id]['image_id'], scores_each[img_id]['caption']))
text_file.write('\n cider {0:2f}'.format(scores_each[img_id]['CIDEr']))
text_file.write('\n')
text_file.close()
# Switch back to training mode
model.train()
return loss_sum/loss_evals, predictions, lang_stats
def extract_split(model, crit, loader, eval_kwargs={}):
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))
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 0)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
use_rela = eval_kwargs.get('use_rela', 0)
use_ssg = eval_kwargs.get('use_ssg', 0)
which_to_extract = eval_kwargs.get('which_to_extract', 'e')
root_path = '/home/yangxu/project/self-critical.pytorch/'
rela_dict_path = root_path + 'data/rela_dict.npy'
rela_dict_info = np.load(rela_dict_path)
rela_dict_info = rela_dict_info[()]
rela_dict = rela_dict_info['rela_dict']
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
save_t = 0
save_id = 1
rnn_size = 1000
N_save = 50
print('total num to save {0}'.format(int(110000)/(N_save*loader.batch_size)))
if which_to_extract == 'h':
save_path = 'save_hs'+ '/' + eval_kwargs.get('checkpoint_path', 0) + '/'
elif which_to_extract == 'e':
save_path = 'save_e' + '/' + eval_kwargs.get('checkpoint_path', 0) + '/'
data_save = np.zeros([0, rnn_size])
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
save_t = save_t + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
fc_feats = None
att_feats = None
att_masks = None
ssg_data = None
rela_data = None
if use_rela == 1:
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['rela_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_matrix'][np.arange(loader.batch_size) * loader.seq_per_img],
data['attr_masks'][np.arange(loader.batch_size) * loader.seq_per_img],
data['labels'][np.arange(loader.batch_size) * loader.seq_per_img],
data['masks'][np.arange(loader.batch_size) * loader.seq_per_img]
]
tmp = [torch.from_numpy(_).cuda() for _ in tmp]
fc_feats, att_feats, att_masks, rela_matrix, rela_masks, attr_matrix, attr_masks, \
labels, masks = tmp
rela_data = {}
rela_data['att_feats'] = att_feats
rela_data['att_masks'] = att_masks
rela_data['rela_matrix'] = rela_matrix
rela_data['rela_masks'] = rela_masks
rela_data['attr_matrix'] = attr_matrix
rela_data['attr_masks'] = attr_masks