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model_msc.py
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model_msc.py
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from datetime import datetime
import os
import sys
import time
import numpy as np
import tensorflow as tf
from PIL import Image
from network import *
from utils import ImageReader, decode_labels, inv_preprocess, prepare_label, write_log, read_labeled_image_list
"""
This script trains or evaluates the model on augmented PASCAL VOC 2012 dataset.
The training set contains 10581 training images.
The validation set contains 1449 validation images.
Training:
'poly' learning rate
different learning rates for different layers
"""
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
class Model_msc(object):
def __init__(self, sess, conf):
self.sess = sess
self.conf = conf
# train
def train(self):
self.train_setup()
self.sess.run(tf.global_variables_initializer())
# Load the pre-trained model if provided
if self.conf.pretrain_file is not None:
self.load(self.loader, self.conf.pretrain_file)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=self.coord, sess=self.sess)
# Train!
for step in range(self.conf.num_steps+1):
start_time = time.time()
feed_dict = { self.curr_step : step }
loss_value = 0
# Clear the accumulated gradients.
self.sess.run(self.zero_op, feed_dict=feed_dict)
# Accumulate gradients.
for i in range(self.conf.grad_update_every):
_, l_val = self.sess.run([self.accum_grads_op, self.reduced_loss], feed_dict=feed_dict)
loss_value += l_val
# Normalise the loss.
loss_value /= self.conf.grad_update_every
# Apply gradients.
if step % self.conf.save_interval == 0:
images, labels, summary, _ = self.sess.run(
[self.image_batch,
self.label_batch,
self.total_summary,
self.train_op],
feed_dict=feed_dict)
self.summary_writer.add_summary(summary, step)
self.save(self.saver, step)
else:
self.sess.run(self.train_op, feed_dict=feed_dict)
duration = time.time() - start_time
print('step {:d} \t loss = {:.3f}, ({:.3f} sec/step)'.format(step, loss_value, duration))
write_log('{:d}, {:.3f}'.format(step, loss_value), self.conf.logfile)
# finish
self.coord.request_stop()
self.coord.join(threads)
# evaluate
def test(self):
self.test_setup()
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
# load checkpoint
checkpointfile = self.conf.modeldir+ '/model.ckpt-' + str(self.conf.valid_step)
self.load(self.loader, checkpointfile)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=self.coord, sess=self.sess)
# Test!
confusion_matrix = np.zeros((self.conf.num_classes, self.conf.num_classes), dtype=np.int)
for step in range(self.conf.valid_num_steps):
preds, _, _, c_matrix = self.sess.run([self.pred, self.accu_update_op, self.mIou_update_op, self.confusion_matrix])
confusion_matrix += c_matrix
if step % 100 == 0:
print('step {:d}'.format(step))
print('Pixel Accuracy: {:.3f}'.format(self.accu.eval(session=self.sess)))
print('Mean IoU: {:.3f}'.format(self.mIoU.eval(session=self.sess)))
self.compute_IoU_per_class(confusion_matrix)
# finish
self.coord.request_stop()
self.coord.join(threads)
# prediction
def predict(self):
self.predict_setup()
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
# load checkpoint
checkpointfile = self.conf.modeldir+ '/model.ckpt-' + str(self.conf.valid_step)
self.load(self.loader, checkpointfile)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=self.coord, sess=self.sess)
# img_name_list
image_list, _ = read_labeled_image_list('', self.conf.test_data_list)
# Predict!
for step in range(self.conf.test_num_steps):
preds = self.sess.run(self.pred)
img_name = image_list[step].split('/')[2].split('.')[0]
# Save raw predictions, i.e. each pixel is an integer between [0,20].
im = Image.fromarray(preds[0,:,:,0], mode='L')
filename = '/%s_mask.png' % (img_name)
im.save(self.conf.out_dir + '/prediction' + filename)
# Save predictions for visualization.
# See utils/label_utils.py for color setting
# Need to be modified based on datasets.
if self.conf.visual:
msk = decode_labels(preds, num_classes=self.conf.num_classes)
im = Image.fromarray(msk[0], mode='RGB')
filename = '/%s_mask_visual.png' % (img_name)
im.save(self.conf.out_dir + '/visual_prediction' + filename)
if step % 100 == 0:
print('step {:d}'.format(step))
print('The output files has been saved to {}'.format(self.conf.out_dir))
# finish
self.coord.request_stop()
self.coord.join(threads)
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
h, w = (self.conf.input_height, self.conf.input_width)
input_size = (h, w)
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(
self.conf.data_dir,
self.conf.data_list,
input_size,
self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label,
IMG_MEAN,
self.coord)
self.image_batch, self.label_batch = reader.dequeue(self.conf.batch_size)
image_batch_075 = tf.image.resize_images(self.image_batch, [int(h * 0.75), int(w * 0.75)])
image_batch_05 = tf.image.resize_images(self.image_batch, [int(h * 0.5), int(w * 0.5)])
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
with tf.variable_scope('', reuse=False):
net = Deeplab_v2(self.image_batch, self.conf.num_classes, True)
with tf.variable_scope('', reuse=True):
net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, True)
with tf.variable_scope('', reuse=True):
net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, True)
# Variables that load from pre-trained model.
restore_var = [v for v in tf.global_variables() if 'fc' not in v.name]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [v for v in all_trainable if 'fc' not in v.name] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'fc' in v.name]
else:
with tf.variable_scope('', reuse=False):
net = ResNet_segmentation(self.image_batch, self.conf.num_classes, True, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net075 = ResNet_segmentation(image_batch_075, self.conf.num_classes, True, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net05 = ResNet_segmentation(image_batch_05, self.conf.num_classes, True, self.conf.encoder_name)
# Variables that load from pre-trained model.
restore_var = [v for v in tf.global_variables() if 'resnet_v1' in v.name]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [v for v in all_trainable if 'resnet_v1' in v.name] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'decoder' in v.name]
decoder_w_trainable = [v for v in decoder_trainable if 'weights' in v.name or 'gamma' in v.name] # lr * 10.0
decoder_b_trainable = [v for v in decoder_trainable if 'biases' in v.name or 'beta' in v.name] # lr * 20.0
# Check
assert(len(all_trainable) == len(decoder_trainable) + len(encoder_trainable))
assert(len(decoder_trainable) == len(decoder_w_trainable) + len(decoder_b_trainable))
# Network raw output
raw_output100 = net.outputs
raw_output075 = net075.outputs
raw_output05 = net05.outputs
raw_output = tf.reduce_max(tf.stack([raw_output100,
tf.image.resize_images(raw_output075, tf.shape(raw_output100)[1:3,]),
tf.image.resize_images(raw_output05, tf.shape(raw_output100)[1:3,])]), axis=0)
# Groud Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch, tf.stack(raw_output.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False) # [batch_size, h, w]
label_proc075 = prepare_label(self.label_batch, tf.stack(raw_output075.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False)
label_proc05 = prepare_label(self.label_batch, tf.stack(raw_output05.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False)
raw_gt = tf.reshape(label_proc, [-1,])
raw_gt075 = tf.reshape(label_proc075, [-1,])
raw_gt05 = tf.reshape(label_proc05, [-1,])
indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
indices075 = tf.squeeze(tf.where(tf.less_equal(raw_gt075, self.conf.num_classes - 1)), 1)
indices05 = tf.squeeze(tf.where(tf.less_equal(raw_gt05, self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32)
gt05 = tf.cast(tf.gather(raw_gt05, indices05), tf.int32)
raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes])
raw_prediction100 = tf.reshape(raw_output100, [-1, self.conf.num_classes])
raw_prediction075 = tf.reshape(raw_output075, [-1, self.conf.num_classes])
raw_prediction05 = tf.reshape(raw_output05, [-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
prediction100 = tf.gather(raw_prediction100, indices)
prediction075 = tf.gather(raw_prediction075, indices075)
prediction05 = tf.gather(raw_prediction05, indices05)
# Pixel-wise softmax_cross_entropy loss
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
loss100 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction100, labels=gt)
loss075 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction075, labels=gt075)
loss05 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction05, labels=gt05)
# L2 regularization
l2_losses = [self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable if 'weights' in v.name]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.reduce_mean(loss100) + tf.reduce_mean(loss075) + tf.reduce_mean(loss05) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - self.curr_step / self.conf.num_steps), self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_encoder = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum)
opt_decoder_w = tf.train.MomentumOptimizer(learning_rate * 10.0, self.conf.momentum)
opt_decoder_b = tf.train.MomentumOptimizer(learning_rate * 20.0, self.conf.momentum)
# Gradient accumulation
# Define a variable to accumulate gradients.
accum_grads = [tf.Variable(tf.zeros_like(v.initialized_value()),
trainable=False) for v in encoder_trainable + decoder_w_trainable + decoder_b_trainable]
# Define an operation to clear the accumulated gradients for next batch.
self.zero_op = [v.assign(tf.zeros_like(v)) for v in accum_grads]
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable)
# Accumulate and normalise the gradients.
self.accum_grads_op = [accum_grads[i].assign_add(grad / self.conf.grad_update_every) for i, grad in enumerate(grads)]
grads = tf.gradients(self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable)
grads_encoder = accum_grads[:len(encoder_trainable)]
grads_decoder_w = accum_grads[len(encoder_trainable) : (len(encoder_trainable) + len(decoder_w_trainable))]
grads_decoder_b = accum_grads[(len(encoder_trainable) + len(decoder_w_trainable)):]
# Update params
train_op_conv = opt_encoder.apply_gradients(zip(grads_encoder, encoder_trainable))
train_op_fc_w = opt_decoder_w.apply_gradients(zip(grads_decoder_w, decoder_w_trainable))
train_op_fc_b = opt_decoder_b.apply_gradients(zip(grads_decoder_b, decoder_b_trainable))
# Finally, get the train_op!
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) # for collecting moving_mean and moving_variance
with tf.control_dependencies(update_ops):
self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b)
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=0)
# Loader for loading the pre-trained model
self.loader = tf.train.Saver(var_list=restore_var)
# Training summary
# Processed predictions: for visualisation.
raw_output_up = tf.image.resize_bilinear(raw_output, input_size)
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
# Image summary.
images_summary = tf.py_func(inv_preprocess, [self.image_batch, 1, IMG_MEAN], tf.uint8)
labels_summary = tf.py_func(decode_labels, [self.label_batch, 1, self.conf.num_classes], tf.uint8)
preds_summary = tf.py_func(decode_labels, [self.pred, 1, self.conf.num_classes], tf.uint8)
self.total_summary = tf.summary.image('images',
tf.concat(axis=2, values=[images_summary, labels_summary, preds_summary]),
max_outputs=1) # Concatenate row-wise.
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
self.summary_writer = tf.summary.FileWriter(self.conf.logdir, graph=tf.get_default_graph())
def test_setup(self):
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(
self.conf.data_dir,
self.conf.valid_data_list,
None, # the images have different sizes
False, # no data-aug
False, # no data-aug
self.conf.ignore_label,
IMG_MEAN,
self.coord)
image, label = reader.image, reader.label # [h, w, 3 or 1]
# Add one batch dimension [1, h, w, 3 or 1]
self.image_batch, self.label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(label, dim=0)
h_orig, w_orig = tf.to_float(tf.shape(self.image_batch)[1]), tf.to_float(tf.shape(self.image_batch)[2])
image_batch_075 = tf.image.resize_images(self.image_batch, tf.stack([tf.to_int32(tf.multiply(h_orig, 0.75)), tf.to_int32(tf.multiply(w_orig, 0.75))]))
image_batch_05 = tf.image.resize_images(self.image_batch, tf.stack([tf.to_int32(tf.multiply(h_orig, 0.5)), tf.to_int32(tf.multiply(w_orig, 0.5))]))
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
with tf.variable_scope('', reuse=False):
net = Deeplab_v2(self.image_batch, self.conf.num_classes, False)
with tf.variable_scope('', reuse=True):
net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, False)
with tf.variable_scope('', reuse=True):
net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, False)
else:
with tf.variable_scope('', reuse=False):
net = ResNet_segmentation(self.image_batch, self.conf.num_classes, False, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net075 = ResNet_segmentation(image_batch_075, self.conf.num_classes, False, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net05 = ResNet_segmentation(image_batch_05, self.conf.num_classes, False, self.conf.encoder_name)
# predictions
# Network raw output
raw_output100 = net.outputs
raw_output075 = net075.outputs
raw_output05 = net05.outputs
raw_output = tf.reduce_max(tf.stack([raw_output100,
tf.image.resize_images(raw_output075, tf.shape(raw_output100)[1:3,]),
tf.image.resize_images(raw_output05, tf.shape(raw_output100)[1:3,])]), axis=0)
raw_output = tf.image.resize_bilinear(raw_output, tf.shape(self.image_batch)[1:3,])
raw_output = tf.argmax(raw_output, axis=3)
pred = tf.expand_dims(raw_output, dim=3)
self.pred = tf.reshape(pred, [-1,])
# labels
gt = tf.reshape(self.label_batch, [-1,])
# Ignoring all labels greater than or equal to n_classes.
temp = tf.less_equal(gt, self.conf.num_classes - 1)
weights = tf.cast(temp, tf.int32)
# fix for tf 1.3.0
gt = tf.where(temp, gt, tf.cast(temp, tf.uint8))
# Pixel accuracy
self.accu, self.accu_update_op = tf.contrib.metrics.streaming_accuracy(
self.pred, gt, weights=weights)
# mIoU
self.mIoU, self.mIou_update_op = tf.contrib.metrics.streaming_mean_iou(
self.pred, gt, num_classes=self.conf.num_classes, weights=weights)
# confusion matrix
self.confusion_matrix = tf.contrib.metrics.confusion_matrix(
self.pred, gt, num_classes=self.conf.num_classes, weights=weights)
# Loader for loading the checkpoint
self.loader = tf.train.Saver(var_list=tf.global_variables())
def predict_setup(self):
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(
self.conf.data_dir,
self.conf.test_data_list,
None, # the images have different sizes
False, # no data-aug
False, # no data-aug
self.conf.ignore_label,
IMG_MEAN,
self.coord)
image, label = reader.image, reader.label # [h, w, 3 or 1]
# Add one batch dimension [1, h, w, 3 or 1]
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(label, dim=0)
h_orig, w_orig = tf.to_float(tf.shape(image_batch)[1]), tf.to_float(tf.shape(image_batch)[2])
image_batch_075 = tf.image.resize_images(image_batch, tf.stack([tf.to_int32(tf.multiply(h_orig, 0.75)), tf.to_int32(tf.multiply(w_orig, 0.75))]))
image_batch_05 = tf.image.resize_images(image_batch, tf.stack([tf.to_int32(tf.multiply(h_orig, 0.5)), tf.to_int32(tf.multiply(w_orig, 0.5))]))
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
with tf.variable_scope('', reuse=False):
net = Deeplab_v2(image_batch, self.conf.num_classes, False)
with tf.variable_scope('', reuse=True):
net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, False)
with tf.variable_scope('', reuse=True):
net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, False)
else:
with tf.variable_scope('', reuse=False):
net = ResNet_segmentation(image_batch, self.conf.num_classes, False, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net075 = ResNet_segmentation(image_batch_075, self.conf.num_classes, False, self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net05 = ResNet_segmentation(image_batch_05, self.conf.num_classes, False, self.conf.encoder_name)
# predictions
# Network raw output
raw_output100 = net.outputs
raw_output075 = net075.outputs
raw_output05 = net05.outputs
raw_output = tf.reduce_max(tf.stack([raw_output100,
tf.image.resize_images(raw_output075, tf.shape(raw_output100)[1:3,]),
tf.image.resize_images(raw_output05, tf.shape(raw_output100)[1:3,])]), axis=0)
raw_output = tf.image.resize_bilinear(raw_output, tf.shape(image_batch)[1:3,])
raw_output = tf.argmax(raw_output, axis=3)
self.pred = tf.cast(tf.expand_dims(raw_output, dim=3), tf.uint8)
# Create directory
if not os.path.exists(self.conf.out_dir):
os.makedirs(self.conf.out_dir)
os.makedirs(self.conf.out_dir + '/prediction')
if self.conf.visual:
os.makedirs(self.conf.out_dir + '/visual_prediction')
# Loader for loading the checkpoint
self.loader = tf.train.Saver(var_list=tf.global_variables())
def save(self, saver, step):
'''
Save weights.
'''
model_name = 'model.ckpt'
checkpoint_path = os.path.join(self.conf.modeldir, model_name)
if not os.path.exists(self.conf.modeldir):
os.makedirs(self.conf.modeldir)
saver.save(self.sess, checkpoint_path, global_step=step)
print('The checkpoint has been created.')
def load(self, saver, filename):
'''
Load trained weights.
'''
saver.restore(self.sess, filename)
print("Restored model parameters from {}".format(filename))
def compute_IoU_per_class(self, confusion_matrix):
mIoU = 0
for i in range(self.conf.num_classes):
# IoU = true_positive / (true_positive + false_positive + false_negative)
TP = confusion_matrix[i,i]
FP = np.sum(confusion_matrix[:, i]) - TP
FN = np.sum(confusion_matrix[i]) - TP
IoU = TP / (TP + FP + FN)
print ('class %d: %.3f' % (i, IoU))
mIoU += IoU / self.conf.num_classes
print ('mIoU: %.3f' % mIoU)