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train_svhn_without_stn.py
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train_svhn_without_stn.py
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# -*- coding: utf-8 -*-
'''
# @Time : 19-6-10 下午9:43
# @Author : LXF && ZXP && WZD
# @FileName: train_svhn_without_stn.py
---------------------
'''
import keras.backend as K
from data_manager import *
from visualizer import plot_mnist_sample
from visualizer import print_evaluation
from visualizer import plot_mnist_grid
from STN import STN_SVHN,SVHN
from keras.utils import plot_model
from keras.optimizers import SGD, Adam, RMSprop
import h5py
from PIL import Image
from keras.callbacks import TensorBoard, EarlyStopping
from keras.utils import np_utils
batch_size = 256
num_epochs = 30
##load data
train_dataset = h5py.File("data/train/digitStruct.mat", "r")
test_dataset = h5py.File("data/test/digitStruct.mat", "r")
def getName(dataset, index):
names = dataset["digitStruct"]["name"]
return ''.join([chr(c[0]) for c in dataset[names[index][0]].value])
def bboxHelper(dataset, attr):
if (len(attr) > 1):
attr = [dataset[attr.value[j].item()].value[0][0] for j in range(len(attr))]
else:
attr = [attr.value[0][0]]
return attr
def getBbox(dataset, index):
item = dataset[dataset["digitStruct"]["bbox"][index].item()]
return {
"height": bboxHelper(dataset, item["height"]),
"label": bboxHelper(dataset, item["label"]),
"left": bboxHelper(dataset, item["left"]),
"top": bboxHelper(dataset, item["top"]),
"width": bboxHelper(dataset, item["width"]),
}
def getWholeBox(dataset, index, im):
bbox = getBbox(dataset, index)
im_left = min(bbox["left"])
im_top = min(bbox["top"])
im_height = max(bbox["top"]) + max(bbox["height"]) - im_top
im_width = max(bbox["left"]) + max(bbox["width"]) - im_left
im_top = im_top - im_height * 0.05 # a bit higher
im_left = im_left - im_width * 0.05 # a bit wider
im_bottom = min(im.size[1], im_top + im_height * 1.05)
im_right = min(im.size[0], im_left + im_width * 1.05)
return {
"label": bbox["label"],
"left": im_left,
"top": im_top,
"right": im_right,
"bottom": im_bottom
}
# Load Train Data
train_count = train_dataset["digitStruct"]["name"].shape[0]
X_train = np.ndarray(shape=(train_count, 32, 32, 3), dtype='float32')
y = {
0: np.zeros(train_count),
1: np.ones(train_count) * 10,
2: np.ones(train_count) * 10,
3: np.ones(train_count) * 10,
4: np.ones(train_count) * 10,
5: np.ones(train_count) * 10
}
print(y[0].shape)
for i in range(train_count):
im = Image.open("data/train/" + getName(train_dataset, i))
box = getWholeBox(train_dataset, i, im)
if len(box["label"]) > 3:
continue
im = im.crop((box["left"], box["top"], box["right"], box["bottom"])).resize((32, 32))
X_train[i, :, :, :] = np.array(im.resize((32, 32)), dtype='float32')
labels = box["label"]
y[0][i] = len(labels)
for j in range(0, 3):
if j < len(labels):
if labels[j] == 10:
y[j + 1][i] = 10
else:
y[j + 1][i] = int(labels[j])
else:
y[j + 1][i] = 10
y_train = [
np_utils.to_categorical(y[0]),
np_utils.to_categorical(y[1]),
np_utils.to_categorical(y[2]),
np_utils.to_categorical(y[3])
]
print(y_train[0].shape)
# Load Test Data
test_count = test_dataset["digitStruct"]["name"].shape[0]
X_test = np.ndarray(shape=(test_count, 32, 32, 3), dtype='float32')
y = {
0: np.zeros(test_count),
1: np.ones(test_count) * 10,
2: np.ones(test_count) * 10,
3: np.ones(test_count) * 10,
4: np.ones(test_count) * 10,
5: np.ones(test_count) * 10
}
for i in range(test_count):
im = Image.open("data/test/" + getName(test_dataset, i))
box = getWholeBox(test_dataset, i, im)
if len(box["label"]) > 3:
continue
im = im.crop((box["left"], box["top"], box["right"], box["bottom"])).resize((32, 32))
X_test[i, :, :, :] = np.array(im.resize((32, 32)), dtype='float32')
labels = box["label"]
y[0][i] = len(labels)
for j in range(0, 3):
if j < len(labels):
if labels[j] == 10:
y[j + 1][i] = 10
else:
y[j + 1][i] = int(labels[j])
else:
y[j + 1][i] = 10
# if i % 500 == 0:
# print(i, len(y[0]))
y_test = [
np_utils.to_categorical(y[0]),
np_utils.to_categorical(y[1]),
np_utils.to_categorical(y[2]),
np_utils.to_categorical(y[3])
]
# model = STN_SVHN()
model = SVHN()
model.compile(loss='categorical_crossentropy', optimizer='sgd')
model.summary()
plot_model(model, to_file='model.png')
tb = TensorBoard('log/SVHN', write_graph=True, write_images=True)
# es = EarlyStopping(monitor='val_acc', patience=5, verbose=0)
callbacks = [tb]
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
model.fit(X_train, y_train, epochs=50, batch_size=64, callbacks=callbacks,
shuffle=True, verbose=1, validation_data=(X_test, y_test))
model.save('model/SVHN/' + 'model.h5')