VSAG/vision_sjtu_19
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

反小陀螺前置代码已完成。

Browse Source
This commit is contained in:
xinyang
2019-07-16 16:31:58 +08:00
parent 298c4c9114
commit 73230d91f1
24 changed files with 18873 additions and 18675 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
tools/TrainCNN/backward.py
View File

@@ -54,16 +54,14 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 10000
BATCH = 30
STEPS = 20000
BATCH = 50
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_DECAY = 0.99
MOVING_AVERAGE_DECAY = 0.99
def train(dataset, show_bar=False):
test_images, test_labels = dataset.all_test_sets()
x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
keep_rate = tf.placeholder(tf.float32)
@@ -107,7 +105,8 @@ def train(dataset, show_bar=False):
)
if i % 100 == 0:
if i % 1000 == 0:
if i % 500 == 0:
test_images, test_labels = dataset.sample_test_sets(10000)
acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
bar.set_postfix({"loss": loss_value, "acc": acc})
@@ -116,6 +115,9 @@ def train(dataset, show_bar=False):
vars_val = sess.run(vars)
save_para("/home/xinyang/Workspace/RM_auto-aim/tools/para", vars_val)
print("save done!")
# pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
# return vars_val, nodes_val
DevList = mvsdk.CameraEnumerateDevice()
@@ -204,9 +206,9 @@ def train(dataset, show_bar=False):
if __name__ == "__main__":
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
dataset = generate.DataSet("/home/xinyang/Workspace/box_cut")
train(dataset, show_bar=True)
input("press enter to continue...")

4
tools/TrainCNN/forward.py
View File

@@ -35,10 +35,10 @@ CONV1_OUTPUT_CHANNELS = 6
CONV2_KERNAL_SIZE = 3
# 第二层卷积输出通道数
CONV2_OUTPUT_CHANNELS = 10
CONV2_OUTPUT_CHANNELS = 12
# 第一层全连接宽度
FC1_OUTPUT_NODES = 16
FC1_OUTPUT_NODES = 20
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15

11
tools/TrainCNN/generate.py
View File

@@ -40,7 +40,7 @@ class DataSet:
files = os.listdir(dir)
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg":
if random.random() > 0.2:
if random.random() > 0.7:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
else:
@@ -61,6 +61,15 @@ class DataSet:
labels.append(self.train_labels[id])
return np.array(samples), np.array(labels)
def sample_test_sets(self, length):
samples = []
labels = []
for i in range(length):
id = random.randint(0, len(self.test_samples)-1)
samples.append(self.test_samples[id])
labels.append(self.test_labels[id])
return np.array(samples), np.array(labels)
def all_train_sets(self):
return self.train_samples[:], self.train_labels[:]