"""Inception-v1 Inflated 3D ConvNet used for Kinetics CVPR paper.
The model is introduced in:
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira, Andrew Zisserman
https://arxiv.org/abs/1705.07750v1
"""
from __future__ import print_function
from __future__ import absolute_import
import warnings
import numpy as np
import argparse
import os
from keras import layers
from keras.models import Model
from keras.layers import Activation, Dense, Input, BatchNormalization, Conv3D, Lambda
from keras.layers import MaxPooling3D, AveragePooling3D, Dropout, Reshape, GlobalAveragePooling3D
from keras.engine.topology import get_source_inputs
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras import backend as K
WEIGHTS_NAME = ['rgb_kinetics_only', 'flow_kinetics_only', 'rgb_imagenet_and_kinetics', 'flow_imagenet_and_kinetics']
from core import utils, const as c
# path to pretrained models with top (classification layer)
WEIGHTS_PATH = {
'rgb_kinetics_only': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/rgb_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels.h5',
'flow_kinetics_only': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/flow_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels.h5',
'rgb_imagenet_and_kinetics': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/rgb_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels.h5',
'flow_imagenet_and_kinetics': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/flow_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels.h5'
}
# path to pretrained models with no top (no classification layer)
WEIGHTS_PATH_NO_TOP = {
'rgb_kinetics_only': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/rgb_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels_no_top.h5',
'flow_kinetics_only': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/flow_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels_no_top.h5',
'rgb_imagenet_and_kinetics': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/rgb_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels_no_top.h5',
'flow_imagenet_and_kinetics': 'https://github.com/dlpbc/keras-kinetics-i3d/releases/download/v0.2/flow_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels_no_top.h5'
}
def _obtain_input_shape(input_shape, default_frame_size, min_frame_size, default_num_frames, min_num_frames, data_format, require_flatten, weights=None):
"""Internal utility to compute/validate the model's input shape.
(Adapted from `keras/applications/imagenet_utils.py`)
# Arguments
input_shape: either None (will return the default nets input shape),
or a user-provided shape to be validated.
default_frame_size: default input frames(images) width/height for the model.
min_frame_size: minimum input frames(images) width/height accepted by the model.
default_num_frames: default input number of frames(images) for the model.
min_num_frames: minimum input number of frames accepted by the model.
data_format: image data format to use.
require_flatten: whether the model is expected to
be linked to a classifier via a Flatten layer.
weights: one of `None` (random initialization)
or 'kinetics_only' (pre-training on Kinetics dataset).
or 'imagenet_and_kinetics' (pre-training on ImageNet and Kinetics datasets).
If weights='kinetics_only' or weights=='imagenet_and_kinetics' then
input channels must be equal to 3.
# Returns
An integer shape tuple (may include None entries).
# Raises
ValueError: in case of invalid argument values.
"""
if weights != 'kinetics_only' and weights != 'imagenet_and_kinetics' and input_shape and len(input_shape) == 4:
if data_format == 'channels_first':
if input_shape[0] not in {1, 3}:
warnings.warn(
'This model usually expects 1 or 3 input channels. '
'However, it was passed an input_shape with ' +
str(input_shape[0]) + ' input channels.')
default_shape = (input_shape[0], default_num_frames, default_frame_size, default_frame_size)
else:
if input_shape[-1] not in {1, 3}:
warnings.warn(
'This model usually expects 1 or 3 input channels. '
'However, it was passed an input_shape with ' +
str(input_shape[-1]) + ' input channels.')
default_shape = (default_num_frames, default_frame_size, default_frame_size, input_shape[-1])
else:
if data_format == 'channels_first':
default_shape = (3, default_num_frames, default_frame_size, default_frame_size)
else:
default_shape = (default_num_frames, default_frame_size, default_frame_size, 3)
if (weights == 'kinetics_only' or weights == 'imagenet_and_kinetics') and require_flatten:
if input_shape is not None:
if input_shape != default_shape:
raise ValueError('When setting`include_top=True` '
'and loading `imagenet` weights, '
'`input_shape` should be ' +
str(default_shape) + '.')
return default_shape
if input_shape:
if data_format == 'channels_first':
if input_shape is not None:
if len(input_shape) != 4:
raise ValueError(
'`input_shape` must be a tuple of four integers.')
if input_shape[0] != 3 and (weights == 'kinetics_only' or weights == 'imagenet_and_kinetics'):
raise ValueError('The input must have 3 channels; got '
'`input_shape=' + str(input_shape) + '`')
if input_shape[1] is not None and input_shape[1] < min_num_frames:
raise ValueError('Input number of frames must be at least ' +
str(min_num_frames) + '; got '
'`input_shape=' + str(input_shape) + '`')
if ((input_shape[2] is not None and input_shape[2] < min_frame_size) or
(input_shape[3] is not None and input_shape[3] < min_frame_size)):
raise ValueError('Input size must be at least ' +
str(min_frame_size) + 'x' + str(min_frame_size) + '; got '
'`input_shape=' + str(input_shape) + '`')
else:
if input_shape is not None:
if len(input_shape) != 4:
raise ValueError(
'`input_shape` must be a tuple of four integers.')
if input_shape[-1] != 3 and (weights == 'kinetics_only' or weights == 'imagenet_and_kinetics'):
raise ValueError('The input must have 3 channels; got '
'`input_shape=' + str(input_shape) + '`')
if input_shape[0] is not None and input_shape[0] < min_num_frames:
raise ValueError('Input number of frames must be at least ' +
str(min_num_frames) + '; got '
'`input_shape=' + str(input_shape) + '`')
if ((input_shape[1] is not None and input_shape[1] < min_frame_size) or
(input_shape[2] is not None and input_shape[2] < min_frame_size)):
raise ValueError('Input size must be at least ' +
str(min_frame_size) + 'x' + str(min_frame_size) + '; got '
'`input_shape=' + str(input_shape) + '`')
else:
if require_flatten:
input_shape = default_shape
else:
if data_format == 'channels_first':
input_shape = (3, None, None, None)
else:
input_shape = (None, None, None, 3)
if require_flatten:
if None in input_shape:
raise ValueError('If `include_top` is True, '
'you should specify a static `input_shape`. '
'Got `input_shape=' + str(input_shape) + '`')
return input_shape
def conv3d_bn(x, filters, num_frames, num_row, num_col, padding='same', strides=(1, 1, 1), use_bias=False, use_activation_fn=True, use_bn=True, name=None):
"""Utility function to apply conv3d + BN.
# Arguments
x: input tensor.
filters: filters in `Conv3D`.
num_frames: frames (time depth) of the convolution kernel.
num_row: height of the convolution kernel.
num_col: width of the convolution kernel.
padding: padding mode in `Conv3D`.
strides: strides in `Conv3D`.
use_bias: use bias or not
use_activation_fn: use an activation function or not.
use_bn: use batch normalization or not.
name: name of the ops; will become `name + '_conv'`
for the convolution and `name + '_bn'` for the
batch norm layer.
# Returns
Output tensor after applying `Conv3D` and `BatchNormalization`.
"""
if name is not None:
bn_name = name + '_bn'
conv_name = name + '_conv'
else:
bn_name = None
conv_name = None
x = Conv3D(filters, (num_frames, num_row, num_col), strides=strides, padding=padding, use_bias=use_bias, name=conv_name)(x)
if use_bn:
if K.image_data_format() == 'channels_first':
bn_axis = 1
else:
bn_axis = 4
x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
if use_activation_fn:
x = Activation('relu', name=name)(x)
return x
def Inception_Inflated3d(include_top=True, weights=None, input_tensor=None, input_shape=None, dropout_prob=0.0, endpoint_logit=True, classes=400):
"""Instantiates the Inflated 3D Inception v1 architecture.
Optionally loads weights pre-trained
on Kinetics. Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The data format
convention used by the model is the one
specified in your Keras config file.
Note that the default input frame(image) size for this model is 224x224.
# Arguments
include_top: whether to include the the classification
layer at the top of the nets.
weights: one of `None` (random initialization)
or 'kinetics_only' (pre-training on Kinetics dataset only).
or 'imagenet_and_kinetics' (pre-training on ImageNet and Kinetics datasets).
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(NUM_FRAMES, 224, 224, 3)` (with `channels_last` data format)
or `(NUM_FRAMES, 3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels.
NUM_FRAMES should be no smaller than 8. The authors used 64
frames per example for training and testing on kinetics dataset
Also, Width and height should be no smaller than 32.
E.g. `(64, 150, 150, 3)` would be one valid value.
dropout_prob: optional, dropout probability applied in dropout layer
after global average pooling layer.
0.0 means no dropout is applied, 1.0 means dropout is applied to all features.
Note: Since Dropout is applied just before the classification
layer, it is only useful when `include_top` is set to True.
endpoint_logit: (boolean) optional. If True, the model's forward pass
will end at producing logits. Otherwise, softmax is applied after producing
the logits to produce the class probabilities prediction. Setting this parameter
to True is particularly useful when you want to combine results of rgb model
and optical flow model.
- `True` end model forward pass at logit output
- `False` go further after logit to produce softmax predictions
Note: This parameter is only useful when `include_top` is set to True.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in WEIGHTS_NAME or weights is None or os.path.exists(weights)):
raise ValueError('The `weights` argument should be either `None` (random initialization) or %s' % str(WEIGHTS_NAME) + ' or a valid path to a file containing `weights` values')
if weights in WEIGHTS_NAME and include_top and classes != 400:
raise ValueError('If using `weights` as one of these %s, with `include_top` as true, `classes` should be 400' % str(WEIGHTS_NAME))
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape, default_frame_size=224, min_frame_size=32, default_num_frames=64, min_num_frames=8, data_format=K.image_data_format(), require_flatten=include_top, weights=weights)
if input_tensor is None:
img_input = Input(shape=input_shape)
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
if K.image_data_format() == 'channels_first':
channel_axis = 1
else:
channel_axis = 4
# Downsampling via convolution (spatial and temporal)
x = conv3d_bn(img_input, 64, 7, 7, 7, strides=(2, 2, 2), padding='same', name='Conv3d_1a_7x7')
# Downsampling (spatial only)
x = MaxPooling3D((1, 3, 3), strides=(1, 2, 2), padding='same', name='MaxPool2d_2a_3x3')(x)
x = conv3d_bn(x, 64, 1, 1, 1, strides=(1, 1, 1), padding='same', name='Conv3d_2b_1x1')
x = conv3d_bn(x, 192, 3, 3, 3, strides=(1, 1, 1), padding='same', name='Conv3d_2c_3x3')
# Downsampling (spatial only)
x = MaxPooling3D((1, 3, 3), strides=(1, 2, 2), padding='same', name='MaxPool2d_3a_3x3')(x)
# Mixed 3b
branch_0 = conv3d_bn(x, 64, 1, 1, 1, padding='same', name='Conv3d_3b_0a_1x1')
branch_1 = conv3d_bn(x, 96, 1, 1, 1, padding='same', name='Conv3d_3b_1a_1x1')
branch_1 = conv3d_bn(branch_1, 128, 3, 3, 3, padding='same', name='Conv3d_3b_1b_3x3')
branch_2 = conv3d_bn(x, 16, 1, 1, 1, padding='same', name='Conv3d_3b_2a_1x1')
branch_2 = conv3d_bn(branch_2, 32, 3, 3, 3, padding='same', name='Conv3d_3b_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_3b_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 32, 1, 1, 1, padding='same', name='Conv3d_3b_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_3b')
# Mixed 3c
branch_0 = conv3d_bn(x, 128, 1, 1, 1, padding='same', name='Conv3d_3c_0a_1x1')
branch_1 = conv3d_bn(x, 128, 1, 1, 1, padding='same', name='Conv3d_3c_1a_1x1')
branch_1 = conv3d_bn(branch_1, 192, 3, 3, 3, padding='same', name='Conv3d_3c_1b_3x3')
branch_2 = conv3d_bn(x, 32, 1, 1, 1, padding='same', name='Conv3d_3c_2a_1x1')
branch_2 = conv3d_bn(branch_2, 96, 3, 3, 3, padding='same', name='Conv3d_3c_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_3c_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 64, 1, 1, 1, padding='same', name='Conv3d_3c_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_3c')
# Downsampling (spatial and temporal)
x = MaxPooling3D((3, 3, 3), strides=(2, 2, 2), padding='same', name='MaxPool2d_4a_3x3')(x)
# Mixed 4b
branch_0 = conv3d_bn(x, 192, 1, 1, 1, padding='same', name='Conv3d_4b_0a_1x1')
branch_1 = conv3d_bn(x, 96, 1, 1, 1, padding='same', name='Conv3d_4b_1a_1x1')
branch_1 = conv3d_bn(branch_1, 208, 3, 3, 3, padding='same', name='Conv3d_4b_1b_3x3')
branch_2 = conv3d_bn(x, 16, 1, 1, 1, padding='same', name='Conv3d_4b_2a_1x1')
branch_2 = conv3d_bn(branch_2, 48, 3, 3, 3, padding='same', name='Conv3d_4b_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_4b_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 64, 1, 1, 1, padding='same', name='Conv3d_4b_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_4b')
# Mixed 4c
branch_0 = conv3d_bn(x, 160, 1, 1, 1, padding='same', name='Conv3d_4c_0a_1x1')
branch_1 = conv3d_bn(x, 112, 1, 1, 1, padding='same', name='Conv3d_4c_1a_1x1')
branch_1 = conv3d_bn(branch_1, 224, 3, 3, 3, padding='same', name='Conv3d_4c_1b_3x3')
branch_2 = conv3d_bn(x, 24, 1, 1, 1, padding='same', name='Conv3d_4c_2a_1x1')
branch_2 = conv3d_bn(branch_2, 64, 3, 3, 3, padding='same', name='Conv3d_4c_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_4c_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 64, 1, 1, 1, padding='same', name='Conv3d_4c_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_4c')
# Mixed 4d
branch_0 = conv3d_bn(x, 128, 1, 1, 1, padding='same', name='Conv3d_4d_0a_1x1')
branch_1 = conv3d_bn(x, 128, 1, 1, 1, padding='same', name='Conv3d_4d_1a_1x1')
branch_1 = conv3d_bn(branch_1, 256, 3, 3, 3, padding='same', name='Conv3d_4d_1b_3x3')
branch_2 = conv3d_bn(x, 24, 1, 1, 1, padding='same', name='Conv3d_4d_2a_1x1')
branch_2 = conv3d_bn(branch_2, 64, 3, 3, 3, padding='same', name='Conv3d_4d_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_4d_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 64, 1, 1, 1, padding='same', name='Conv3d_4d_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_4d')
# Mixed 4e
branch_0 = conv3d_bn(x, 112, 1, 1, 1, padding='same', name='Conv3d_4e_0a_1x1')
branch_1 = conv3d_bn(x, 144, 1, 1, 1, padding='same', name='Conv3d_4e_1a_1x1')
branch_1 = conv3d_bn(branch_1, 288, 3, 3, 3, padding='same', name='Conv3d_4e_1b_3x3')
branch_2 = conv3d_bn(x, 32, 1, 1, 1, padding='same', name='Conv3d_4e_2a_1x1')
branch_2 = conv3d_bn(branch_2, 64, 3, 3, 3, padding='same', name='Conv3d_4e_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_4e_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 64, 1, 1, 1, padding='same', name='Conv3d_4e_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_4e')
# Mixed 4f
branch_0 = conv3d_bn(x, 256, 1, 1, 1, padding='same', name='Conv3d_4f_0a_1x1')
branch_1 = conv3d_bn(x, 160, 1, 1, 1, padding='same', name='Conv3d_4f_1a_1x1')
branch_1 = conv3d_bn(branch_1, 320, 3, 3, 3, padding='same', name='Conv3d_4f_1b_3x3')
branch_2 = conv3d_bn(x, 32, 1, 1, 1, padding='same', name='Conv3d_4f_2a_1x1')
branch_2 = conv3d_bn(branch_2, 128, 3, 3, 3, padding='same', name='Conv3d_4f_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_4f_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 128, 1, 1, 1, padding='same', name='Conv3d_4f_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_4f')
# Downsampling (spatial and temporal)
x = MaxPooling3D((2, 2, 2), strides=(2, 2, 2), padding='same', name='MaxPool2d_5a_2x2')(x)
# Mixed 5b
branch_0 = conv3d_bn(x, 256, 1, 1, 1, padding='same', name='Conv3d_5b_0a_1x1')
branch_1 = conv3d_bn(x, 160, 1, 1, 1, padding='same', name='Conv3d_5b_1a_1x1')
branch_1 = conv3d_bn(branch_1, 320, 3, 3, 3, padding='same', name='Conv3d_5b_1b_3x3')
branch_2 = conv3d_bn(x, 32, 1, 1, 1, padding='same', name='Conv3d_5b_2a_1x1')
branch_2 = conv3d_bn(branch_2, 128, 3, 3, 3, padding='same', name='Conv3d_5b_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_5b_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 128, 1, 1, 1, padding='same', name='Conv3d_5b_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_5b')
# Mixed 5c
branch_0 = conv3d_bn(x, 384, 1, 1, 1, padding='same', name='Conv3d_5c_0a_1x1')
branch_1 = conv3d_bn(x, 192, 1, 1, 1, padding='same', name='Conv3d_5c_1a_1x1')
branch_1 = conv3d_bn(branch_1, 384, 3, 3, 3, padding='same', name='Conv3d_5c_1b_3x3')
branch_2 = conv3d_bn(x, 48, 1, 1, 1, padding='same', name='Conv3d_5c_2a_1x1')
branch_2 = conv3d_bn(branch_2, 128, 3, 3, 3, padding='same', name='Conv3d_5c_2b_3x3')
branch_3 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same', name='MaxPool2d_5c_3a_3x3')(x)
branch_3 = conv3d_bn(branch_3, 128, 1, 1, 1, padding='same', name='Conv3d_5c_3b_1x1')
x = layers.concatenate([branch_0, branch_1, branch_2, branch_3], axis=channel_axis, name='Mixed_5c')
if include_top:
# Classification block
x = AveragePooling3D((2, 7, 7), strides=(1, 1, 1), padding='valid', name='global_avg_pool')(x)
x = Dropout(dropout_prob)(x)
x = conv3d_bn(x, classes, 1, 1, 1, padding='same', use_bias=True, use_activation_fn=False, use_bn=False, name='Conv3d_6a_1x1')
num_frames_remaining = int(x.shape[1])
x = Reshape((num_frames_remaining, classes))(x)
# logits (raw scores for each class)
x = Lambda(lambda x: K.mean(x, axis=1, keepdims=False), output_shape=lambda s: (s[0], s[2]))(x)
if not endpoint_logit:
x = Activation('softmax', name='prediction')(x)
else:
# h = int(x.shape[2])
# w = int(x.shape[3])
# x = AveragePooling3D((2, h, w), strides=(1, 1, 1), padding='valid', name='global_avg_pool')(x)
pass
inputs = img_input
# create model
model = Model(inputs, x, name='i3d_inception')
# load weights
if weights in WEIGHTS_NAME:
model_name = None
if weights == WEIGHTS_NAME[0]: # rgb_kinetics_only
if include_top:
model_name = 'rgb_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels.h5'
else:
model_name = 'rgb_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels_no_top.h5'
elif weights == WEIGHTS_NAME[1]: # flow_kinetics_only
if include_top:
model_name = 'flow_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels.h5'
else:
model_name = 'flow_inception_i3d_kinetics_only_tf_dim_ordering_tf_kernels_no_top.h5'
elif weights == WEIGHTS_NAME[2]: # rgb_imagenet_and_kinetics
if include_top:
model_name = 'rgb_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels.h5'
else:
model_name = 'rgb_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels_no_top.h5'
elif weights == WEIGHTS_NAME[3]: # flow_imagenet_and_kinetics
if include_top:
model_name = 'flow_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels.h5'
else:
model_name = 'flow_inception_i3d_imagenet_and_kinetics_tf_dim_ordering_tf_kernels_no_top.h5'
weights_path = '%s/Charades/baseline_models/i3d-keras/%s' % (c.DATA_ROOT_PATH, model_name)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def extract_features():
'''
Loads pretrained model of I3d Inception architecture for the paper: 'https://arxiv.org/abs/1705.07750'
Evaluates a RGB and Flow sample similar to the paper's github repo: 'https://github.com/deepmind/kinetics-i3d'
'''
# parse arguments
# parser = argparse.ArgumentParser()
# parser.add_argument('--eval-type',
# help='specify model type. 1 stream (rgb or flow) or 2 stream (joint = rgb and flow).',
# type=str, choices=['rgb', 'flow', 'joint'], default='joint')
#
# parser.add_argument('--no-imagenet-pretrained',
# help='If set, load model weights trained only on kinetics dataset. Otherwise, load model weights trained on imagenet and kinetics dataset.',
# action='store_true')
#
# args = parser.parse_args()
################
NUM_FRAMES = 8
FRAME_HEIGHT = 224
FRAME_WIDTH = 224
NUM_RGB_CHANNELS = 3
NUM_FLOW_CHANNELS = 2
NUM_CLASSES = 400
root_path = c.DATA_ROOT_PATH
input_sample = np.zeros((1, 16, 224, 224, 3), dtype=np.float32)
input_shape = (8, 224, 224, 3)
# build model for RGB data
# and load pretrained weights (trained on imagenet and kinetics dataset)
rgb_model = Inception_Inflated3d(include_top=False, weights='rgb_imagenet_and_kinetics', input_shape=input_shape, classes=NUM_CLASSES)
# make prediction
rgb_logits = rgb_model.predict(input_sample, verbose=2)
# produce softmax output from model logit for class probabilities
sample_logits = sample_logits[0] # we are dealing with just one example
sample_predictions = np.exp(sample_logits) / np.sum(np.exp(sample_logits))
sorted_indices = np.argsort(sample_predictions)[::-1]
print('\nNorm of logits: %f' % np.linalg.norm(sample_logits))
print('\nTop classes and probabilities')
for index in sorted_indices[:20]:
print(sample_predictions[index], sample_logits[index], kinetics_classes[index])
def evaluate_model():
'''
Loads pretrained model of I3d Inception architecture for the paper: 'https://arxiv.org/abs/1705.07750'
Evaluates a RGB and Flow sample similar to the paper's github repo: 'https://github.com/deepmind/kinetics-i3d'
'''
# parse arguments
# parser = argparse.ArgumentParser()
# parser.add_argument('--eval-type',
# help='specify model type. 1 stream (rgb or flow) or 2 stream (joint = rgb and flow).',
# type=str, choices=['rgb', 'flow', 'joint'], default='joint')
#
# parser.add_argument('--no-imagenet-pretrained',
# help='If set, load model weights trained only on kinetics dataset. Otherwise, load model weights trained on imagenet and kinetics dataset.',
# action='store_true')
#
# args = parser.parse_args()
################
NUM_FRAMES = 79
FRAME_HEIGHT = 224
FRAME_WIDTH = 224
NUM_RGB_CHANNELS = 3
NUM_FLOW_CHANNELS = 2
NUM_CLASSES = 400
root_path = c.DATA_ROOT_PATH
LABEL_MAP_PATH = '%s/Charades/baseline_models/i3d-keras/label_map.txt' % (root_path)
SAMPLE_DATA_PATH = {
'rgb': '%s/Charades/baseline_models/i3d-keras/v_CricketShot_g04_c01_rgb.npy' % (root_path),
'flow': '%s/Charades/baseline_models/i3d-keras/v_CricketShot_g04_c01_flow.npy' % (root_path)}
kinetics_classes = [x.strip() for x in open(LABEL_MAP_PATH, 'r')]
is_rgb = True
is_joint = False
is_trained_kinetics_only = False
if is_rgb:
if is_trained_kinetics_only:
# build model for RGB data
# and load pretrained weights (trained on kinetics dataset only)
rgb_model = Inception_Inflated3d(include_top=True, weights='rgb_kinetics_only', input_shape=(NUM_FRAMES, FRAME_HEIGHT, FRAME_WIDTH, NUM_RGB_CHANNELS), classes=NUM_CLASSES)
else:
# build model for RGB data
# and load pretrained weights (trained on imagenet and kinetics dataset)
rgb_model = Inception_Inflated3d(include_top=True, weights='rgb_imagenet_and_kinetics', input_shape=(NUM_FRAMES, FRAME_HEIGHT, FRAME_WIDTH, NUM_RGB_CHANNELS), classes=NUM_CLASSES)
# load RGB sample (just one example)
rgb_sample = np.load(SAMPLE_DATA_PATH['rgb'])
# make prediction
rgb_logits = rgb_model.predict(rgb_sample, verbose=2)
else:
if is_trained_kinetics_only:
# build model for optical flow data
# and load pretrained weights (trained on kinetics dataset only)
flow_model = Inception_Inflated3d(include_top=True, weights='flow_kinetics_only', input_shape=(NUM_FRAMES, FRAME_HEIGHT, FRAME_WIDTH, NUM_FLOW_CHANNELS), classes=NUM_CLASSES)
else:
# build model for optical flow data
# and load pretrained weights (trained on imagenet and kinetics dataset)
flow_model = Inception_Inflated3d(include_top=True, weights='flow_imagenet_and_kinetics', input_shape=(NUM_FRAMES, FRAME_HEIGHT, FRAME_WIDTH, NUM_FLOW_CHANNELS), classes=NUM_CLASSES)
# load flow sample (just one example)
flow_sample = np.load(SAMPLE_DATA_PATH['flow'])
# make prediction
flow_logits = flow_model.predict(flow_sample, verbose=2)
# produce final model logits
if is_rgb and not is_joint:
sample_logits = rgb_logits
elif not is_rgb and not is_joint:
sample_logits = flow_logits
elif is_joint: # joint
sample_logits = rgb_logits + flow_logits
else:
raise ('Sorry, model type is not defined, either rgb, or flow or joint!')
# produce softmax output from model logit for class probabilities
sample_logits = sample_logits[0] # we are dealing with just one example
sample_predictions = np.exp(sample_logits) / np.sum(np.exp(sample_logits))
sorted_indices = np.argsort(sample_predictions)[::-1]
print('\nNorm of logits: %f' % np.linalg.norm(sample_logits))
print('\nTop classes and probabilities')
for index in sorted_indices[:20]:
print(sample_predictions[index], sample_logits[index], kinetics_classes[index])
return
