Finetune the pretrained model on UCF101

Hi , When I Finetune the pretrained model on UCF101, I adapt the evaluate_sampe.py, only use rgb' input, change the _NUM_CLASSES` to 101, add loss and optimizer after the logits, feed the training data and label to net, but I encounter the error messages:

2017-09-02 15:28:24.771133: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-02 15:28:24.771169: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-02 15:28:24.771190: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-02 15:28:24.771194: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-02 15:28:24.771198: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-09-02 15:28:25.113985: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:84:00.0
Total memory: 10.91GiB
Free memory: 2.11GiB
2017-09-02 15:28:25.114035: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-09-02 15:28:25.114043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y
2017-09-02 15:28:25.114067: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:84:00.0)
INFO:tensorflow:Restoring parameters from data/checkpoints/rgb_scratch/model.ckpt
Traceback (most recent call last):
  File "i3d_finetune_ucf101.py", line 175, in <module>
    tf.app.run(main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "i3d_finetune_ucf101.py", line 133, in main
    feed_dict={rgb_input:batch_xs, rgb_y: batch_ys})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1100, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (8, 101) for Tensor u'Placeholder_1:0', which has shape '(?, 400)'

Here is my python file:

# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Loads a sample video and classifies using a trained Kinetics checkpoint."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf

import i3d
from  dataset import Dataset

batch_size = 8
training_iter = 1000
learning_rate = 0.001

_IMAGE_SIZE = 227
_NUM_CLASSES = 101

_SAMPLE_VIDEO_FRAMES = 79
_SAMPLE_PATHS = {
    'rgb': 'data/v_CricketShot_g04_c01_rgb.npy',
    'flow': 'data/v_CricketShot_g04_c01_flow.npy',
}

_CHECKPOINT_PATHS = {
    'rgb': 'data/checkpoints/rgb_scratch/model.ckpt',
    'flow': 'data/checkpoints/flow_scratch/model.ckpt',
    'rgb_imagenet': 'data/checkpoints/rgb_imagenet/model.ckpt',
    'flow_imagenet': 'data/checkpoints/flow_imagenet/model.ckpt',
}

_LABEL_MAP_PATH = 'data/label_map.txt'

FLAGS = tf.flags.FLAGS

tf.flags.DEFINE_string('eval_type', 'rgb', 'rgb, flow, or joint')
tf.flags.DEFINE_boolean('imagenet_pretrained', True, '')


def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)
  eval_type = FLAGS.eval_type
  imagenet_pretrained = FLAGS.imagenet_pretrained

  if eval_type not in ['rgb', 'flow', 'joint']:
    raise ValueError('Bad `eval_type`, must be one of rgb, flow, joint')

  kinetics_classes = [x.strip() for x in open(_LABEL_MAP_PATH)]

  if eval_type in ['rgb', 'joint']:
    # RGB input has 3 channels.
    rgb_input = tf.placeholder(
        tf.float32,
        shape=(batch_size, 10, _IMAGE_SIZE, _IMAGE_SIZE, 3))
    rgb_y = tf.placeholder(tf.float32, [None, _NUM_CLASSES])
    with tf.variable_scope('RGB'):
      rgb_model = i3d.InceptionI3d(
          _NUM_CLASSES, spatial_squeeze=False, final_endpoint='Logits')
      rgb_logits, _ = rgb_model(
          rgb_input, is_training=True, dropout_keep_prob=1.0)
    rgb_variable_map = {}
    for variable in tf.global_variables():
      if variable.name.split('/')[0] == 'RGB':
        rgb_variable_map[variable.name.replace(':0', '')] = variable
        print('===variable:', variable)
    rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)
#    print('=====variables', rgb_variable_map)

  if eval_type in ['flow', 'joint']:
    # Flow input has only 2 channels.
    flow_input = tf.placeholder(
        tf.float32,
        shape=(1, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 2))
    with tf.variable_scope('Flow'):
      flow_model = i3d.InceptionI3d(
          _NUM_CLASSES, spatial_squeeze=True, final_endpoint='Logits')
      flow_logits, _ = flow_model(
          flow_input, is_training=False, dropout_keep_prob=1.0)
    flow_variable_map = {}
    for variable in tf.global_variables():
      if variable.name.split('/')[0] == 'Flow':
        flow_variable_map[variable.name.replace(':0', '')] = variable
    flow_saver = tf.train.Saver(var_list=flow_variable_map, reshape=True)

  if eval_type == 'rgb':
    model_logits = rgb_logits
  elif eval_type == 'flow':
    model_logits = flow_logits
  else:
    model_logits = rgb_logits + flow_logits
  model_predictions = tf.nn.softmax(model_logits)
  print( '===model_predictions.shape:', model_predictions.shape)
  model_predictions = tf.reduce_mean(model_predictions, (1,2))
  print( '===model_predictions.shape:', model_predictions.shape)
  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model_predictions, labels=rgb_y))
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)

  dataset = Dataset('data/rgb_train_split1.txt', 'data/rgb_test_split1.txt')
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True
  with tf.Session(config=config) as sess:
    step = 1
    while step < training_iter:
      batch_xs, batch_ys = dataset.next_batch(batch_size, 'train')
      rgb_saver.restore(sess, _CHECKPOINT_PATHS['rgb'])
      sess.run(
        optimizer,
        feed_dict={rgb_input:batch_xs, rgb_y: batch_ys})

if __name__ == '__main__':
  tf.app.run(main)

[prakashjayy]

_SAMPLE_VIDEO_FRAMES = 64
_IMAGE_SIZE = 224
_NUM_CLASSES = 101
_EPOCHS = 10
_BATCH_SIZE = 4

_FILE_LOC_TRAIN = glob.glob("data/train/*.npy")
print("[Total Files: {}]".format(len(_FILE_LOC_TRAIN)))

_MEAN_DATA = np.load("data/mean_data__ucf.npy")[np.newaxis, :, :, :, :]
TRAINING = True
print("Mean_Data: {}".format(_MEAN_DATA.shape))

rgb_input = tf.placeholder(
    tf.float32,
    shape=(None, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 3))

y_true = tf.placeholder(
    tf.float32,
    shape=(None, _NUM_CLASSES))

with tf.variable_scope('RGB'):
  rgb_model = i3d.InceptionI3d(_NUM_CLASSES, spatial_squeeze=True, final_endpoint='Mixed_5c')
  rgb_net, _ = rgb_model( rgb_input, is_training=False, dropout_keep_prob=1.0)
  end_point = 'Logits'
  with tf.variable_scope(end_point):
    rgb_net = tf.nn.avg_pool3d(rgb_net, ksize=[1, 2, 7, 7, 1],
                           strides=[1, 1, 1, 1, 1], padding=snt.VALID)
    if TRAINING:
        rgb_net = tf.nn.dropout(rgb_net, 0.7)
    logits = i3d.Unit3D(output_channels=_NUM_CLASSES,
                    kernel_shape=[1, 1, 1],
                    activation_fn=None,
                    use_batch_norm=False,
                    use_bias=True,
                    name='Conv3d_0c_1x1')(rgb_net, is_training=True)

    logits = tf.squeeze(logits, [2, 3], name='SpatialSqueeze')
    averaged_logits = tf.reduce_mean(logits, axis=1)

  # predictions = tf.nn.softmax(averaged_logits)


rgb_variable_map = {}

for variable in tf.global_variables():
    if variable.name.split("/")[-4] == "Logits": continue
    if variable.name.split('/')[0] == 'RGB':
        rgb_variable_map[variable.name.replace(':0', '')] = variable

#print(rgb_variable_map)
rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)

model_logits = averaged_logits
model_predictions = tf.nn.softmax(model_logits)

This worked for me.

[joaoluiscarreira]

Hi,

Yes, 25 fps.

Best,

Joao

On Fri, Jan 4, 2019, 8:06 AM ChristopherSTAN <[e-mail] wrote:

_SAMPLE_VIDEO_FRAMES = 64 _IMAGE_SIZE = 224 _NUM_CLASSES = 101 _EPOCHS = 10 _BATCH_SIZE = 4

_FILE_LOC_TRAIN = glob.glob(“data/train/*.npy”) print(“[Total Files: {}]”.format(len(_FILE_LOC_TRAIN)))

_MEAN_DATA = np.load(“data/mean_data__ucf.npy”)[np.newaxis, :, :, :, :] TRAINING = True print(“Mean_Data: {}”.format(_MEAN_DATA.shape))

rgb_input = tf.placeholder( tf.float32, shape=(None, _SAMPLE_VIDEO_FRAMES, _IMAGE_SIZE, _IMAGE_SIZE, 3))

y_true = tf.placeholder( tf.float32, shape=(None, _NUM_CLASSES))

with tf.variable_scope(‘RGB’): rgb_model = i3d.InceptionI3d(_NUM_CLASSES, spatial_squeeze=True, final_endpoint=‘Mixed_5c’) rgb_net, _ = rgb_model( rgb_input, is_training=False, dropout_keep_prob=1.0) end_point = ‘Logits’ with tf.variable_scope(end_point): rgb_net = tf.nn.avg_pool3d(rgb_net, ksize=[1, 2, 7, 7, 1], strides=[1, 1, 1, 1, 1], padding=snt.VALID) if TRAINING: rgb_net = tf.nn.dropout(rgb_net, 0.7) logits = i3d.Unit3D(output_channels=_NUM_CLASSES, kernel_shape=[1, 1, 1], activation_fn=None, use_batch_norm=False, use_bias=True, name=‘Conv3d_0c_1x1’)(rgb_net, is_training=True)

logits = tf.squeeze(logits, [2, 3], name='SpatialSqueeze')
averaged_logits = tf.reduce_mean(logits, axis=1)

predictions = tf.nn.softmax(averaged_logits)

rgb_variable_map = {}

for variable in tf.global_variables(): if variable.name.split(“/”)[-4] == “Logits”: continue if variable.name.split(‘/’)[0] == ‘RGB’: rgb_variable_map[variable.name.replace(‘:0’, ‘’)] = variable

#print(rgb_variable_map) rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)

model_logits = averaged_logits model_predictions = tf.nn.softmax(model_logits)

This worked for me.

Hello,

I want to ask you a question about the data processing.

How do you sample the video clips? 25 frames per second?

Thank you a lot. PS: My English is poor. I am sorry about that.

Chris

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