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test.py
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test.py
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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
import os
import time
import torch
import torch.utils.data
from torch import nn
from transformers import *
import torchvision
from lib import segmentation
import transforms as T
import utils
import numpy as np
from pycocotools import mask
from scipy.misc import imread
def get_dataset(name, image_set, transform, args):
if name == 'refcoco' or name == 'refcoco+':
if args.baseline_bilstm:
from data.dataset_refer_glove import ReferDataset
else:
from data.dataset_refer_bert import ReferDataset
ds = ReferDataset(args,
split=image_set,
image_transforms=transform,
target_transforms=None,
input_size=(256, 448),
eval_mode=True)
num_classes = 2
elif name == 'a2d':
from data.a2d import A2DDataset
ds = A2DDataset(args,
train= image_set == 'train',
db_root_dir= args.a2d_data_root,
transform=transform,
inputRes=(args.size_a2d_x, args.size_a2d_y))
num_classes = 2
elif name == 'davis':
from data.davis2017 import DAVIS17
ds = DAVIS17(args,
train= image_set == 'train',
db_root_dir=args.davis_data_root,
transform=transform,
emb_type=args.emb_type)
num_classes = 2
return ds, num_classes
def evaluate(args, model, data_loader, ref_ids, refer, bert_model, device, num_classes, display=False, baseline_model=None,
objs_ids=None, num_objs_list=None):
model.eval()
confmat = utils.ConfusionMatrix(num_classes)
metric_logger = utils.MetricLogger(delimiter=" ")
refs_ids_list = []
outputs = []
# dict to save results for DAVIS
total_outputs = {}
# evaluation variables
cum_I, cum_U = 0, 0
eval_seg_iou_list = [.5, .6, .7, .8, .9]
seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
seg_total = 0
mean_IoU = []
header = 'Test:'
with torch.no_grad():
k = 0
l = 0
for image, target, sentences, attentions in metric_logger.log_every(data_loader, 100, header):
image, target, sentences, attentions = image.to(device), target.to(device), sentences.to(device), attentions.to(device)
sentences = sentences.squeeze(1)
attentions = attentions.squeeze(1)
if args.dataset == 'davis' or args.dataset == 'a2d':
sentences = sentences.unsqueeze(-1)
attentions = attentions.unsqueeze(-1)
target = target.cpu().data.numpy()
for j in range(sentences.size(-1)):
refs_ids_list.append(k)
if args.baseline_bilstm:
sent = sentences[:, :, :, j]
att = attentions[:, :, j]
num_tokens = torch.sum(att, dim=-1)
processed_seqs = sent[:num_tokens, :]
hidden_states, cell_states = baseline_model[0](processed_seqs)
hidden_states = hidden_states[0]
processed_hidden_states = hidden_states[:num_tokens, :]
last_hidden_states = torch.mean(processed_hidden_states, dim=0)
last_hidden_states = baseline_model[1](last_hidden_states)
embedding = last_hidden_states.unsqueeze(1)
else:
last_hidden_states = bert_model(sentences[:, :, j], attention_mask=attentions[:, :, j])[0]
embedding = last_hidden_states[:, 0, :]
output, _, _ = model(image, embedding.squeeze(1))
output = output['out'].cpu()
output_mask = output.argmax(1).data.numpy()
outputs.append(output_mask)
I, U = computeIoU(output_mask, target)
if U == 0:
this_iou = 0.0
else:
this_iou = I*1.0/U
mean_IoU.append(this_iou)
cum_I += I
cum_U += U
for n_eval_iou in range(len(eval_seg_iou_list)):
eval_seg_iou = eval_seg_iou_list[n_eval_iou]
seg_correct[n_eval_iou] += (this_iou >= eval_seg_iou)
seg_total += 1
del image, target, attentions
if display:
plt.figure()
plt.axis('off')
if args.dataset == 'refcoco' or args.dataset == 'refcoco+':
ref = refer.loadRefs(ref_ids[k])
image_info = refer.Imgs[ref[0]['image_id']]
for p in range(len(ref[0]['sentences'])):
l += 1
if args.dataset == 'refcoco' or args.dataset == 'refcoco+':
sentence = ref[0]['sentences'][p]['raw']
im_path = os.path.join(refer.IMAGE_DIR, image_info['file_name'])
elif args.dataset == 'davis':
idx = ref_ids[k]
sentence = refer[idx]
im_path = os.path.join(args.davis_data_root, img_list[k])
elif args.dataset == 'a2d':
sentence = refer[k]
image_name = ref_ids[k]
im_path = os.path.join(args.a2d_root_dir, image_name)
im = imread(im_path)
plt.imshow(im)
if args.dataset == 'davis':
if img_list[k] not in total_outputs:
total_outputs[img_list[k]] = {}
o_mask = output_mask.copy()
o_mask = o_mask.astype(int)*int(idx.split('_')[-1])
total_outputs[img_list[k]] = o_mask.squeeze(0)
else:
total_outputs[img_list[k]][output_mask.squeeze(0) == True] = int(idx.split('_')[-1])
plt.text(0, 0, sentence, fontsize=12)
ax = plt.gca()
ax.set_autoscale_on(False)
# mask definition
img = np.ones((im.shape[0], im.shape[1], 3))
color_mask = np.array([0, 255, 0]) / 255.0
for i in range(3):
img[:, :, i] = color_mask[i]
if args.dataset == 'refcoco' or args.dataset == 'refcoco+':
output_mask = outputs[-len(ref[0]['sentences'])+p].transpose(1, 2, 0)
ax.imshow(np.dstack((img, output_mask * 0.5)))
if not os.path.isdir(results_folder):
os.makedirs(results_folder)
figname = os.path.join(args.results_folder, str(l) + '.png')
plt.close()
k += 1
if args.dataset == 'davis':
for r in total_outputs.keys():
new_im = Image.fromarray(total_outputs[r].astype(np.uint8))
file_name = r.split('/')[-1].split('.')[0]
folder_name = r.split('/')[-2]
if not os.path.isdir(os.path.join(submission_path, folder_name)):
os.makedirs(os.path.join(submission_path, folder_name))
new_im.save(os.path.join(args.submission_path, folder_name, file_name + '.png'))
mean_IoU = np.array(mean_IoU)
mIoU = np.mean(mean_IoU)
print('Final results:')
print('Mean IoU is %.2f\n' % (mIoU*100.))
results_str = ''
for n_eval_iou in range(len(eval_seg_iou_list)):
results_str += ' precision@%s = %.2f\n' % \
(str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
results_str += ' overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
print(results_str)
return refs_ids_list, outputs
def get_transform():
transforms = []
transforms.append(T.ToTensor())
transforms.append(T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]))
return T.Compose(transforms)
# compute IoU
def computeIoU(pred_seg, gd_seg):
I = np.sum(np.logical_and(pred_seg, gd_seg))
U = np.sum(np.logical_or(pred_seg, gd_seg))
return I, U
def main(args):
device = torch.device(args.device)
dataset_test, _ = get_dataset(args.dataset, args.split, get_transform(), args)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
data_loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=1, sampler=test_sampler,
num_workers=args.workers, collate_fn=utils.collate_fn_emb_berts)
model = segmentation.__dict__[args.model](num_classes=2,
aux_loss=False,
pretrained=False,
args=args)
model.to(device)
model_class = BertModel
bert_model = model_class.from_pretrained(args.ck_bert)
bert_model.to(device)
if args.baseline_bilstm:
bilstm = torch.nn.LSTM(input_size=300, hidden_size=1000, num_layers=1, bidirectional=True, batch_first=True)
fc_layer = torch.nn.Linear(2000, 768)
bilstm = bilstm.to(device)
fc_layer = fc_layer.to(device)
checkpoint = torch.load(args.resume, map_location='cpu')
bert_model.load_state_dict(checkpoint['bert_model'])
model.load_state_dict(checkpoint['model'])
if args.baseline_bilstm:
bilstm.load_state_dict(checkpoint['bilstm'])
fc_layer.load_state_dict(checkpoint['fc_layer'])
if args.dataset == 'refcoco' or args.dataset == 'refcoco+':
ref_ids = dataset_test.ref_ids
refer = dataset_test.refer
ids = ref_ids
objs_ids = None
num_objs_list = None
elif args.dataset == 'davis':
ids = dataset_test.ids
objs_ids = None
num_objs_list = None
with open(args.davis_annotations_file) as f:
lines = f.readlines()
elif args.dataset == 'a2d':
ids = dataset_test.img_list
objs_ids = dataset_test.objs
num_objs_list = dataset_test.num_objs_list
with open(args.davis_annotations_file) as f:
lines = f.readlines()
if args.dataset == 'davis' or args.dataset == 'a2d':
refer = {}
for l in lines:
words = l.split()
refer[words[0] + '_' + words[1]] = {}
refer[words[0] + '_' + words[1]] = ' '.join(words[2:])[1:-1]
if args.baseline_bilstm:
baseline_model = [bilstm, fc_layer]
else:
baseline_model = None
refs_ids_list, outputs = evaluate(args, model, data_loader_test, ids, refer, bert_model, device=device,
num_classes=2, baseline_model=baseline_model, objs_ids=objs_ids, num_objs_list=num_objs_list)
if __name__ == "__main__":
from args import get_parser
parser = get_parser()
args = parser.parse_args()
main(args)