1 files changed, 155 insertions, 0 deletions
diff --git a/Codes/inference-test.py b/Codes/inference-test.py
new file mode 100644
index 0000000..b16a56c
--- /dev/null
+++ b/Codes/inference-test.py
@@ -0,0 +1,155 @@
+import tensorflow as tf
+import os
+import time
+import numpy as np
+import pickle
+
+
+from models import generator
+from utils import DataLoader, load, save, psnr_error
+from constant import const
+import evaluate
+
+
+slim = tf.contrib.slim
+
+os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID"
+os.environ['CUDA_VISIBLE_DEVICES'] = const.GPU
+
+dataset_name = const.DATASET
+test_folder = const.TEST_FOLDER
+
+num_his = const.NUM_HIS
+height, width = 256, 256
+
+snapshot_dir = const.SNAPSHOT_DIR
+psnr_dir = const.PSNR_DIR
+evaluate_name = const.EVALUATE
+
+print(const)
+
+
+# define dataset
+with tf.name_scope('dataset'):
+    test_video_clips_tensor = tf.placeholder(shape=[1, height, width, 3 * (num_his + 1)],
+                                             dtype=tf.float32)
+    test_inputs = test_video_clips_tensor[..., 0:num_his*3]
+    test_gt = test_video_clips_tensor[..., -3:]
+    print('test inputs = {}'.format(test_inputs))
+    print('test prediction gt = {}'.format(test_gt))
+
+# define testing generator function and
+# in testing, only generator networks, there is no discriminator networks and flownet.
+with tf.variable_scope('generator', reuse=None):
+    print('testing = {}'.format(tf.get_variable_scope().name))
+    test_outputs = generator(test_inputs, layers=4, output_channel=3)
+    test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt)
+
+
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+with tf.Session(config=config) as sess:
+    # dataset
+    data_loader = DataLoader(test_folder, height, width)
+
+    # initialize weights
+    sess.run(tf.global_variables_initializer())
+    print('Init global successfully!')
+
+    # tf saver
+    saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None)
+
+    restore_var = [v for v in tf.global_variables()]
+    loader = tf.train.Saver(var_list=restore_var)
+
+    def inference_func(ckpt, dataset_name, evaluate_name):
+        load(loader, sess, ckpt)
+
+        output_records = []
+        videos_info = data_loader.videos
+        num_videos = len(videos_info.keys())
+        total = 0
+        timestamp = time.time()
+
+        for video_name, video in videos_info.items():
+            length = video['length']
+            total += length
+            psnrs = np.empty(shape=(length,), dtype=np.float32)
+
+            for i in range(num_his, length):
+                video_clip = data_loader.get_video_clips(video_name, i - num_his, i + 1)
+                output, psnr = sess.run([test_outputs, test_psnr_error]
+                                feed_dict={test_video_clips_tensor: video_clip[np.newaxis, ...]})
+                outputs[i] = output
+
+                tf.image.encode_png(
+                    output,
+                    compression=-1,
+                    name=None
+                )
+
+                print('video = {} / {}, i = {} / {}, psnr = {:.6f}'.format(
+                    video_name, num_videos, i, length, psnr))
+
+            outputs[0:num_his] = outputs[num_his]
+            output_records.append(outputs)
+
+        result_dict = {'dataset': dataset_name, 'output': output_records, 'flow': [], 'names': [], 'diff_mask': []}
+
+        used_time = time.time() - timestamp
+        print('total time = {}, fps = {}'.format(used_time, total / used_time))
+
+        # TODO specify what's the actual name of ckpt.
+        pickle_path = os.path.join(output_dir, os.path.split(ckpt)[-1])
+        with open(pickle_path, 'wb') as writer:
+            pickle.dump(result_dict, writer, pickle.HIGHEST_PROTOCOL)
+
+        # results = evaluate.evaluate(evaluate_name, pickle_path)
+        # print(results)
+
+
+    if os.path.isdir(snapshot_dir):
+        def check_ckpt_valid(ckpt_name):
+            is_valid = False
+            ckpt = ''
+            if ckpt_name.startswith('model.ckpt-'):
+                ckpt_name_splits = ckpt_name.split('.')
+                ckpt = str(ckpt_name_splits[0]) + '.' + str(ckpt_name_splits[1])
+                ckpt_path = os.path.join(snapshot_dir, ckpt)
+                if os.path.exists(ckpt_path + '.index') and os.path.exists(ckpt_path + '.meta') and \
+                        os.path.exists(ckpt_path + '.data-00000-of-00001'):
+                    is_valid = True
+
+            return is_valid, ckpt
+
+        def scan_psnr_folder():
+            tested_ckpt_in_psnr_sets = set()
+            for test_psnr in os.listdir(psnr_dir):
+                tested_ckpt_in_psnr_sets.add(test_psnr)
+            return tested_ckpt_in_psnr_sets
+
+        def scan_model_folder():
+            saved_models = set()
+            for ckpt_name in os.listdir(snapshot_dir):
+                is_valid, ckpt = check_ckpt_valid(ckpt_name)
+                if is_valid:
+                    saved_models.add(ckpt)
+            return saved_models
+
+        tested_ckpt_sets = scan_psnr_folder()
+        while True:
+            all_model_ckpts = scan_model_folder()
+            new_model_ckpts = all_model_ckpts - tested_ckpt_sets
+
+            for ckpt_name in new_model_ckpts:
+                # inference
+                ckpt = os.path.join(snapshot_dir, ckpt_name)
+                inference_func(ckpt, dataset_name, evaluate_name)
+
+                tested_ckpt_sets.add(ckpt_name)
+
+            print('waiting for models...')
+            # evaluate.evaluate('compute_auc', psnr_dir)
+            time.sleep(60)
+    else:
+        inference_func(snapshot_dir, dataset_name, evaluate_name)