Add image classification models #52

wwhu · 2017-05-24T09:51:33Z

resolve #28
The classification accuracy has not been validated yet.

qingqing01

还需要使用文档，可以参考其他models里的文档，但模型介绍部分觉得不用详细讲了，都是比较经典的模型，book里都有介绍，可以指向链接指向book。

qingqing01 · 2017-05-25T08:15:48Z

image_classification/googlenet.py

+
+def inception(name, input, channels, filter1, filter3R, filter3, filter5R,
+              filter5, proj):
+    cov1 = paddle.layer.conv_projection(


这里采用这个配置里inception2 的写法吧，https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/paddle/image/googlenet.py#L19

conv_projection不适应CPU.

已添加inception2

直接去掉inception，只保留inception2吧~

qingqing01 · 2017-05-25T08:22:45Z

image_classification/resnet.py

+    res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
+    res2 = layer_warp(block_func, res1, 128, stages[1], 2)
+    res3 = layer_warp(block_func, res2, 256, stages[2], 2)
+    res4 = layer_warp(block_func, res3, 512, stages[3], 2)


对于上面 " # TODO: bug fix for ch_in = input.num_filters "，一种办法是这里layer_warp这里可以显示的指定通道数。当然也可以等配置解析重写之后，看能不能获取layer的属性，再改也行~~

输入通道数的用处是判断是否需要在block之间进行projection，我这里直接通过b_projection参数显式指定是否需要projection

明白了，这样也可以，那上面的 TODO 可以删掉了吧~

qingqing01 · 2017-05-25T08:28:24Z

image_classification/train.py

        learning_rate_decay_a=0.1,
        learning_rate_decay_b=128000 * 35,
        learning_rate_schedule="discexp", )

    train_reader = paddle.batch(
-        paddle.reader.shuffle(reader.test_reader("train.list"), buf_size=1000),
+        paddle.reader.shuffle(
+            reader.test_reader(os.path.join(args.data_dir, 'train.list')),


等haoshuang的PR合入之后，需要换成flowers的数据，用加速的reader。同时文档里，可以告诉用户如果换成自己的数据集如何处理~

已在文档里说明如何表示数据

qingqing01 · 2017-05-25T08:32:19Z

image_classification/train.py

+        loss2 = paddle.layer.cross_entropy_cost(
+            input=out2, label=lbl, coeff=0.3)
+        paddle.evaluator.classification_error(input=out2, label=lbl)
+        extra_layers = [loss1, loss2]


要么把net, cost这些代码都放到各自的配置里去？这样train.py看着清爽一些~

把net放到各自配置里了，因为考虑到infer阶段不需要cost，所以cost还是放到了train.py

qingqing01 · 2017-06-01T11:27:14Z

image_classification/googlenet.py

+
+def inception(name, input, channels, filter1, filter3R, filter3, filter5R,
+              filter5, proj):
+    cov1 = paddle.layer.conv_projection(


直接去掉inception，只保留inception2吧~

qingqing01 · 2017-06-01T11:31:49Z

image_classification/README.md

+                                                     BATCH_SIZE),
+    learning_rate=0.001 / BATCH_SIZE,
+    learning_rate_decay_a=0.1,
+    learning_rate_decay_b=128000 * 35,


learning_rate_decay_a， learning_rate_decay_b解释下吧，参见book里的解释~

qingqing01 · 2017-06-01T11:38:53Z

image_classification/resnet.py

+    res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
+    res2 = layer_warp(block_func, res1, 128, stages[1], 2)
+    res3 = layer_warp(block_func, res2, 256, stages[2], 2)
+    res4 = layer_warp(block_func, res3, 512, stages[3], 2)


明白了，这样也可以，那上面的 TODO 可以删掉了吧~

wwhu · 2017-06-02T06:08:14Z

@qingqing01 这几处已修改

qingqing01

再加个infer.py吧。

qingqing01 · 2017-06-05T06:38:28Z

image_classification/resnet.py

+
+
+def layer_warp(block_func, input, features, count, stride):
+    conv = block_func(input, features, stride, True)


第一个block_func的b_projection不是总为True的，还是的依赖于输入conv的channel数。所以我觉得还是shortcut里对判断输入和输出的channel数是否一致，决定是否用conv做升降维度好些~

qingqing01 · 2017-06-06T11:42:24Z

flowers dataset已经merge了，需要换下~

guoshengCS · 2017-06-08T10:05:51Z

能否稍微调整下basicblock和bottleneck中shortcut的顺序，将shortcut放在前面，保证拓扑顺序与原文尽可能一致，thx~

wwhu · 2017-06-13T11:12:15Z

添加了infer.py以及flowers数据，并调整了shortcut位置。
@qingqing01 @guoshengCS

qingqing01 · 2017-06-13T11:31:56Z

image_classification/infer.py

+        # image in RGB mode. It must swap the channel order.
+        im = im[(2, 1, 0), :, :]  # BGR
+        im = im.flatten()
+        im = im / 255.0


这里看下image.py里有没有函数可以直接用。而且这里 im/255.0和训练不对应吧，训练是减去均值吧。

qingqing01 · 2017-06-13T11:34:15Z

image_classification/googlenet.py

+__all__ = ['googlenet']
+
+
+def inception2(name, input, channels, filter1, filter3R, filter3, filter5R,


inception2 -> inception

qingqing01 · 2017-06-13T11:36:57Z

image_classification/vgg.py

@@ -17,7 +17,7 @@
 __all__ = ['vgg13', 'vgg16', 'vgg19']


-def vgg(input, nums):
+def vgg(input, nums, class_dim=100):


class_dim=100 -> class_dim，去掉100的默认值吧

qingqing01 · 2017-06-13T11:43:17Z

image_classification/resnet.py

+    return conv
+
+
+def resnet_imagenet(input, depth=50, class_dim=100):


class_dim=100 -> class_dim，去掉默认值100吧。

所有的默认值都去掉了

lcy-seso · 2017-06-14T23:53:38Z

If you are not going to finish this work. please tell me.

wwhu · 2017-06-15T00:38:28Z

I will finish it ASAP. Sorry for the delay. @lcy-seso

lcy-seso · 2017-06-15T00:44:36Z

@wwhu You are welcome. I think after some modifications, we can try to merge the already finished part and then based on a merged version, refactor, refine, and validate the learning performance. Also, thanks for your work.

qingqing01

LGTM. 精度需要进一步确保~

add v2 API for imagenet models

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wwhu requested a review from qingqing01 May 24, 2017 09:51

qingqing01 reviewed May 25, 2017

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add doc and reorginize net output

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qingqing01 reviewed Jun 1, 2017

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minor revision

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qingqing01 reviewed Jun 5, 2017

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add infer.py and flower dataset

0116bc8

wwhu added 2 commits June 13, 2017 19:34

fix bug for resnet_cifar10 and adjust learning rate

208ca38

fix bug

e9b94ca

qingqing01 reviewed Jun 13, 2017

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wwhu added 2 commits June 15, 2017 10:21

add xmap for image list and modify the image reader of infer.py

bdffa40

resolve conflict

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qingqing01 approved these changes Jun 15, 2017

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lcy-seso merged commit 8e80e20 into PaddlePaddle:develop Jun 15, 2017

wwhu deleted the in-dev branch June 15, 2017 08:15

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Add image classification models #52

Add image classification models #52

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		def layer_warp(block_func, input, features, count, stride):
		conv = block_func(input, features, stride, True)

		__all__ = ['googlenet']


		def inception2(name, input, channels, filter1, filter3R, filter3, filter5R,

		return conv


		def resnet_imagenet(input, depth=50, class_dim=100):

Add image classification models #52

Add image classification models #52

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lcy-seso commented Jun 14, 2017

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