# predict in batches
n_batch = int(math.ceil(len(all_inps) / batch))
for j in range(n_batch):
from_idx = j * batch
to_idx = min(from_idx + batch, len(all_inps))
# collect images input in the batch
this_batch = all_inps[from_idx:to_idx]
inp_feed = my_pool.map(
lambda inp: (np.expand_dims(
tfnet.framework.preprocess(os.path.join(inp[0], inp[1])), 0)),
this_batch)
# Feed to the net
feed_dict = {tfnet.inp: np.concatenate(inp_feed, 0)}
tfnet.say('Forwarding {} inputs ...'.format(len(inp_feed)))
start = time.time()
out = tfnet.sess.run([tfnet.out, tfnet.my_out], feed_dict)
my_out = out[1]
out = out[0]
stop = time.time()
last = stop - start
tfnet.say('Total time = {}s / {} inps = {} ips'.format(
last, len(inp_feed),
len(inp_feed) / last))
# Post processing
tfnet.say('Post processing {} inputs ...'.format(len(inp_feed)))
start = time.time()
my_pool.map(
lambda p: (lambda i, prediction: my_postprocess(
requiredDirectories = [FLAGS.imgdir, FLAGS.binary,
FLAGS.backup, os.path.join(FLAGS.imgdir, 'out')]
if FLAGS.summary:
requiredDirectories.append(FLAGS.summary)
_get_dir(requiredDirectories)
tfnet = TFNet(FLAGS)
model = masknet.create_model()
model.summary()
model.load_weights("weights.hdf5")
elapsed = int()
start = timer()
tfnet.say('Press [ESC] to quit demo')
preprocessed = tfnet.framework.preprocess(frame)
buffer_inp.append(frame)
buffer_pre.append(preprocessed)
# Only process and imshow when queue is full
if elapsed % FLAGS.queue == 0:
feed_dict = {tfnet.inp: buffer_pre}
net_out = tfnet.sess.run(
[tfnet.out, tfnet.my_c2, tfnet.my_c3, tfnet.my_c4, tfnet.my_c5], feed_dict)
my_c2 = net_out[1]
my_c3 = net_out[2]
my_c4 = net_out[3]
my_c5 = net_out[4]
net_out = net_out[0]
to_idx = min(from_idx + batch, len(all_inps))
# collect images input in the batch
this_batch = all_inps[from_idx:to_idx]
# 对batch中每一个图片文件进行多进程操作：
# 在非training的predict中resize，BGR->RGB,使用cv转化为numpy tensor
inp_feed = pool.map(
lambda inp: (np.expand_dims(
tfnet.framework.preprocess(os.path.join(inp_path, inp)), 0)),
this_batch)
# Feed to the net
# 将一个batch内的所有图片concat起来成一个batch送入网络中
feed_dict = {tfnet.inp: np.concatenate(inp_feed, 0)}
tfnet.say('Forwarding {} inputs ...'.format(len(inp_feed)))
start = time.time()
out = tfnet.sess.run(tfnet.out, feed_dict)
stop = time.time()
last = stop - start
tfnet.say('Total time = {}s / {} inps = {} ips'.format(
last, len(inp_feed),
len(inp_feed) / last))
# Post processing
# 将tf的out进行后处理，给图片画框并存储
tfnet.say('Post processing {} inputs ...'.format(len(inp_feed)))
start = time.time()
pool.map(
lambda p: (lambda i, prediction: postprocess(
tfnet, prediction, os.path.join(inp_path, this_batch[i])))(*p),
