In [1]:
import os import cv2 import random import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from tqdm import tqdm from sklearn.utils import shuffle from sklearn.model_selection import train_test_split %matplotlib inline %config InlineBackend.figure_format = 'retina'
导入数据
In [2]:
df_train = pd.read_csv('train.csv', header=None)
df_train.columns = ['image_id', 'class', 'label']
df_train.head()
Out[2]:
| image_id | class | label | |
|---|---|---|---|
| 0 | Images/collar_design_labels/4d8a38b29930a403e5... | collar_design_labels | nnynn |
| 1 | Images/collar_design_labels/bd0981f231180d2b00... | collar_design_labels | nynnn |
| 2 | Images/collar_design_labels/26937e1724feadfe39... | collar_design_labels | ynnnn |
| 3 | Images/collar_design_labels/cf4140ec542887270f... | collar_design_labels | nynnn |
| 4 | Images/collar_design_labels/50644b2b9de045f2d1... | collar_design_labels | nynnn |
In [3]:
classes = ['collar_design_labels', 'neckline_design_labels', 'skirt_length_labels',
'sleeve_length_labels', 'neck_design_labels', 'coat_length_labels', 'lapel_design_labels',
'pant_length_labels']
len(classes)
Out[3]:
8
加载数据
In [4]:
cur_class = classes[0]
df_load = df_train[(df_train['class'] == cur_class)].copy()
df_load.reset_index(inplace=True)
del df_load['index']
print('{0}: {1}'.format(cur_class, len(df_load)))
df_load.head()
collar_design_labels: 8393
Out[4]:
| image_id | class | label | |
|---|---|---|---|
| 0 | Images/collar_design_labels/4d8a38b29930a403e5... | collar_design_labels | nnynn |
| 1 | Images/collar_design_labels/bd0981f231180d2b00... | collar_design_labels | nynnn |
| 2 | Images/collar_design_labels/26937e1724feadfe39... | collar_design_labels | ynnnn |
| 3 | Images/collar_design_labels/cf4140ec542887270f... | collar_design_labels | nynnn |
| 4 | Images/collar_design_labels/50644b2b9de045f2d1... | collar_design_labels | nynnn |
In [5]:
df_load[(df_load.index == 2)]
Out[5]:
| image_id | class | label | |
|---|---|---|---|
| 2 | Images/collar_design_labels/26937e1724feadfe39... | collar_design_labels | ynnnn |
In [6]:
n = len(df_load)
n_class = len(df_load['label'][0])
width = 299
X = np.zeros((n, width, width, 3), dtype=np.uint8)
y = np.zeros((n, n_class), dtype=np.uint8)
for i in tqdm(range(n)):
tmp_label = df_load['label'][i]
if len(tmp_label) > n_class:
print(df_load['image_id'][i])
X[i] = cv2.resize(cv2.imread('./train/{0}'.format(df_load['image_id'][i])), (width, width))
y[i][tmp_label.find('y')] = 1
100%|██████████| 8393/8393 [00:31<00:00, 269.94it/s]
In [7]:
plt.figure(figsize=(12, 7))
for i in range(8):
random_index = random.randint(0, n-1)
plt.subplot(2, 4, i+1)
plt.imshow(X[random_index][:,:,::-1])
plt.title(y[random_index])
提取特征
In [8]:
from keras.layers import * from keras.models import * from keras.callbacks import * from keras.optimizers import * from keras.applications import * from keras.regularizers import * from keras.applications.inception_v3 import preprocess_input
/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend.
In [9]:
cnn_model = InceptionResNetV2(include_top=False, input_shape=(width, width, 3), weights='imagenet') inputs = Input((width, width, 3)) x = inputs x = Lambda(preprocess_input, name='preprocessing')(x) x = cnn_model(x) x = GlobalAveragePooling2D()(x) x = Dropout(0.5)(x) x = Dense(n_class, activation='softmax', name='softmax')(x) model = Model(inputs, x)
划分训练、测试集
In [10]:
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.12, random_state=42) X_train.shape, y_train.shape
Out[10]:
((7385, 299, 299, 3), (7385, 5))
In [11]:
adam = Adam(lr=0.001)
prefix_cls = cur_class.split('_')[0]
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='./models/{0}.best.h5'.format(prefix_cls), verbose=1,
save_best_only=True)
h = model.fit(X_train, y_train, batch_size=32, epochs=80,
callbacks=[EarlyStopping(patience=3), checkpointer],
shuffle=True,
validation_split=0.1)
Train on 6646 samples, validate on 739 samples Epoch 1/80 6624/6646 [============================>.] - ETA: 0s - loss: 0.9380 - acc: 0.6532Epoch 00001: val_loss improved from inf to 1.10376, saving model to ./models/collar.best.h5 6646/6646 [==============================] - 196s 29ms/step - loss: 0.9367 - acc: 0.6539 - val_loss: 1.1038 - val_acc: 0.6766 Epoch 2/80 6624/6646 [============================>.] - ETA: 0s - loss: 0.6084 - acc: 0.7876Epoch 00002: val_loss improved from 1.10376 to 0.75603, saving model to ./models/collar.best.h5 6646/6646 [==============================] - 183s 27ms/step - loss: 0.6077 - acc: 0.7880 - val_loss: 0.7560 - val_acc: 0.7361 Epoch 3/80 6624/6646 [============================>.] - ETA: 0s - loss: 0.4251 - acc: 0.8552Epoch 00003: val_loss did not improve 6646/6646 [==============================] - 181s 27ms/step - loss: 0.4264 - acc: 0.8554 - val_loss: 1.5847 - val_acc: 0.5372 Epoch 4/80 6624/6646 [============================>.] - ETA: 0s - loss: 0.3442 - acc: 0.8819Epoch 00004: val_loss did not improve 6646/6646 [==============================] - 181s 27ms/step - loss: 0.3444 - acc: 0.8816 - val_loss: 0.8974 - val_acc: 0.7294 Epoch 5/80 6624/6646 [============================>.] - ETA: 0s - loss: 0.2424 - acc: 0.9179Epoch 00005: val_loss did not improve 6646/6646 [==============================] - 181s 27ms/step - loss: 0.2420 - acc: 0.9180 - val_loss: 1.4803 - val_acc: 0.6455
In [12]:
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(h.history['loss'])
plt.plot(h.history['val_loss'])
plt.legend(['loss', 'val_loss'])
plt.ylabel('loss')
plt.xlabel('epoch')
plt.subplot(1, 2, 2)
plt.plot(h.history['acc'])
plt.plot(h.history['val_acc'])
plt.legend(['acc', 'val_acc'])
plt.ylabel('acc')
plt.xlabel('epoch')
Out[12]:
Text(0.5,0,'epoch')
In [14]:
model.evaluate(X_train, y_train, batch_size=256)
7385/7385 [==============================] - 62s 8ms/step
Out[14]:
[0.7887161796288307, 0.7599187538037853]
In [15]:
model.evaluate(X_valid, y_valid, batch_size=256)
1008/1008 [==============================] - 9s 9ms/step
Out[15]:
[1.4538595203369382, 0.6448412698412699]
加载测试集
In [16]:
df_test = pd.read_csv('test_a.csv', header=None)
df_test.columns = ['image_id', 'class', 'x']
del df_test['x']
df_test.head()
Out[16]:
| image_id | class | |
|---|---|---|
| 0 | Images/collar_design_labels/60951f5761ea3a01d6... | collar_design_labels |
| 1 | Images/collar_design_labels/677e1183282769a3fe... | collar_design_labels |
| 2 | Images/collar_design_labels/3f5a2be205e5d253eb... | collar_design_labels |
| 3 | Images/collar_design_labels/1712bf3d7b57d779ad... | collar_design_labels |
| 4 | Images/collar_design_labels/0b2b4254f35ce3a41a... | collar_design_labels |
In [17]:
df_load = df_test[(df_test['class'] == cur_class)].copy()
df_load.reset_index(inplace=True)
del df_load['index']
print('{0}: {1}'.format(cur_class, len(df_load)))
df_load.head()
collar_design_labels: 1082
Out[17]:
| image_id | class | |
|---|---|---|
| 0 | Images/collar_design_labels/60951f5761ea3a01d6... | collar_design_labels |
| 1 | Images/collar_design_labels/677e1183282769a3fe... | collar_design_labels |
| 2 | Images/collar_design_labels/3f5a2be205e5d253eb... | collar_design_labels |
| 3 | Images/collar_design_labels/1712bf3d7b57d779ad... | collar_design_labels |
| 4 | Images/collar_design_labels/0b2b4254f35ce3a41a... | collar_design_labels |
In [18]:
n = len(df_load)
X_test = np.zeros((n, width, width, 3), dtype=np.uint8)
for i in tqdm(range(n)):
X_test[i] = cv2.resize(cv2.imread('./test_a/{0}'.format(df_load['image_id'][i])), (width, width))
100%|██████████| 1082/1082 [00:04<00:00, 265.47it/s]
In [19]:
test_np = model.predict(X_test, batch_size=256)
In [20]:
test_np.shape
Out[20]:
(1082, 5)
In [21]:
result = []
for i, row in df_load.iterrows():
tmp_list = test_np[i]
tmp_result = ''
for tmp_ret in tmp_list:
tmp_result += '{:.4f};'.format(tmp_ret)
result.append(tmp_result[:-1])
df_load['result'] = result
df_load.head()
Out[21]:
| image_id | class | result | |
|---|---|---|---|
| 0 | Images/collar_design_labels/60951f5761ea3a01d6... | collar_design_labels | 0.0008;0.9845;0.0005;0.0141;0.0001 |
| 1 | Images/collar_design_labels/677e1183282769a3fe... | collar_design_labels | 0.0000;0.0000;0.0000;1.0000;0.0000 |
| 2 | Images/collar_design_labels/3f5a2be205e5d253eb... | collar_design_labels | 0.0000;0.9998;0.0000;0.0002;0.0000 |
| 3 | Images/collar_design_labels/1712bf3d7b57d779ad... | collar_design_labels | 0.0056;0.0004;0.0087;0.9846;0.0007 |
| 4 | Images/collar_design_labels/0b2b4254f35ce3a41a... | collar_design_labels | 0.0000;0.0000;0.0002;0.9998;0.0000 |
In [22]:
df_load.to_csv('./result/{}_0307a.csv'.format(prefix_cls), header=None, index=False)
prefix_cls
Out[22]:
'collar'