forked from adap/flower
-
Notifications
You must be signed in to change notification settings - Fork 0
/
lecture3.py
202 lines (173 loc) · 8.22 KB
/
lecture3.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
import tensorflow as tf
import numpy as np
import time
import random
from glob import glob
from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras.utils import *
import decord
from decord import VideoReader
import tensorflow_hub as hub
import tensorflow_addons as tfa
def all_from_dir(local_path):
path = np.sort(np.array(glob(local_path)))
samples = list()
labels = list()
for j in range(len(path)):
v = np.sort(np.array(glob(path[j]+'/*')))
random.shuffle(v)
for i in range(len(v)):
samples.append(v[i])
labels.append(j)
return np.array(samples), np.array(labels)
def format_frames(frame, resolution):
output_size = [resolution, resolution]
frame = tf.image.convert_image_dtype(frame, tf.uint8)
frame = tf.image.resize(frame, size=list(output_size))
frame = tf.image.per_image_standardization(frame)
return frame
def get_n_frames(x, num_samples):
# create an array of indices 0 .. num_samples with stepping 1 in tf.float32
indices = tf.linspace(tf.cast(0, tf.float32), tf.cast(num_samples-1, tf.float32), num_samples)
# take at max as many frames as there are in the video clip
indices = tf.clip_by_value(indices, 0, tf.cast(tf.shape(x)[0] - 1, tf.float32))
# convert indices to tf.int32 -- important for cases where we have less than num_samples frames!
indices = tf.cast(tf.round(indices), tf.int32)
# return as many frames from input video as 'indices' tells
return tf.gather(x, indices, axis=0)
# return video from file_path given frame_count and resolution
def get_clip(file_path, frame_count, resolution):
vr = VideoReader(file_path)
frames = vr.get_batch(range(len(vr))).asnumpy()
video_tensor = format_frames(frames, resolution)
frames = get_n_frames(video_tensor, frame_count)
return frames
def ran_crop(vido):
n1,n2,n3,n4 =tf.shape(vido).numpy()
dcrop = tf.image.random_crop(vido, size=(n1,n2//2, n2//2 ,3))
return np.array (tf.image.resize(dcrop,[n2,n3]))
def video_augment(vio):
ff1 = np.random.rand(1)
if 0.0 <= ff1 <= .4:
vio = tf.image.flip_left_right(vio)
vio = tf.image.random_brightness(vio, 0.2)
vio = tf.image.random_saturation(vio, 5, 10)
elif 0.41 <= ff1 <= .8:
vio = tf.image.flip_up_down(vio)
vio = tf.image.random_hue(vio, 0.2)
vio = tf.image.random_contrast(vio, 0.2, 0.5)
else:
vio = ran_crop(vio)
return vio
def prepare_dataset(data, batch_size, frame_count, resolution, n_class, shuffle=True, augment=False):
num_samples = len(data)
if shuffle:
random.shuffle(data)
# return video content in snippets of batch_size for up to num_samples of videos
for offset in range(0, num_samples, batch_size):
# *_batch is a list of batch_size file names
video_batch = np.array(data[offset:offset+batch_size]) [:,0]
label_batch = np.array(data[offset:offset+batch_size]) [:,1]
# Initialise X_train and y_train arrays for this batch
X_train = []
y_train = []
# load each video by filename (get_clip), trainsform each label to categorical type
for (sample, label) in zip(video_batch, label_batch):
label = to_categorical(label, n_class)
video_clip = get_clip(sample, frame_count, resolution)
if augment:
video_clip = video_augment(video_clip)
X_train.append(video_clip)
y_train.append(label)
X_train = np.float32(np.array(X_train))
y_train = np.array(y_train)
yield X_train, y_train
def create_network(net1, input_img, class_count):
y0 = Lambda(lambda x: tf.transpose(x, perm=(0,4,1,2,3)))(input_img)
y0 = net1(y0)
y0 = Lambda(lambda x: tf.transpose(x, perm=(0,2,3,4,1)))(y0)
y0 = tfa.layers.AdaptiveAveragePooling3D((1, 1, 1))(y0)
y0 = Flatten()(y0)
y0 = Dense(512, activation='relu')(y0)
y0 = Dropout(.3)(y0)
y = Dense(class_count , activation='softmax')(y0)
return y
# declaring the training step as a separate @tf.function can save from out-of-memory error
@tf.function
def video_train_step(x_batch_train, y_batch_train, model, optimizer, loss_fn, train_acc_metric):
with tf.GradientTape() as tape:
logits = model(x_batch_train, training=True)
loss_value = loss_fn(y_batch_train, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
train_acc_metric.update_state(y_batch_train, logits)
return loss_value
def video_val_step(val_dataset, model, val_acc_metric):
for x_batch_val, y_batch_val in val_dataset:
val_logits = model(x_batch_val, training=False)
val_acc_metric.update_state(y_batch_val, val_logits)
return val_acc_metric.result()
def video_fit(train, val, model, optimizer, loss_fn, batch_size, epochs, frame_count, class_count):
train_acc_metric = tf.keras.metrics.CategoricalAccuracy()
val_acc_metric = tf.keras.metrics.CategoricalAccuracy()
for epoch in range(epochs):
train_dataset = prepare_dataset(train, batch_size, frame_count, resolution, class_count, shuffle=True, augment=False)
val_dataset = prepare_dataset(val, batch_size, frame_count, resolution, class_count, shuffle=True, augment=False)
print("\nStart of epoch %d" % (epoch,))
start_time = time.time()
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
print(x_batch_train.shape)
loss_value = video_train_step(x_batch_train, y_batch_train, model, optimizer, loss_fn, train_acc_metric)
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
# Display metrics at the end of each epoch.
train_acc = train_acc_metric.result()
print("Training acc over epoch: %.4f" % (float(train_acc),))
# Reset training metrics at the end of each epoch
train_acc_metric.reset_states()
# Run a validation loop at the end of each epoch.
val_acc = video_val_step(val_dataset, model, val_acc_metric)
print("Validation acc: %.4f" % (float(val_acc),))
val_acc_metric.reset_states()
print("Time taken: %.2fs" % (time.time() - start_time))
return model
# --- load training and validation data ---
train_path = './kin6/train/*'
val_path = './kin6/test/*'
iv = 10 # small portion of the val set for after epoch testing
train_samples, train_labels = all_from_dir(train_path)
train_set = list(zip(train_samples, train_labels))
tmp_samples, tmp_labels = all_from_dir(val_path)
total_test_samples = len(tmp_labels)
val_samples = tmp_samples[0:iv]
val_labels = tmp_labels[0:iv]
test_samples = tmp_samples[iv:total_test_samples]
test_labels = tmp_labels[iv:total_test_samples]
val_set = list(zip(val_samples, val_labels))
test_set = list(zip(test_samples, test_labels))
class_count = np.max(train_labels)+1
print("Number of train videos: " + str(len(train_samples)))
print("Number of val videos: " + str(len(val_samples)))
print("Number of test videos: " + str(len(test_samples)))
print("Classes to learn: " + str(class_count))
# --- load base models and create network ---
resolution = 224 # Load SwinFormer feature extractor
epochs = 1
batch_size = 4
frame_count = 32
swin = hub.KerasLayer("https://tfhub.dev/shoaib6174/swin_base_patch244_window877_kinetics600_22k/1", trainable=False)
input_img = tf.keras.layers.Input(shape=(frame_count, resolution, resolution, 3))
y = create_network(swin, input_img, class_count)
model = Model(input_img, y)
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-4)
loss_fn = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])
model = video_fit(train_set, val_set, model, optimizer, loss_fn, batch_size, epochs, frame_count, class_count)
test_dataset = prepare_dataset(test_set, batch_size, frame_count, resolution, class_count, shuffle=True, augment=False)
test_acc = video_val_step(test_dataset, model, tf.keras.metrics.CategoricalAccuracy())
print("Test acc: %.4f" % (float(test_acc),))