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長方形の画像について画像認識を行いたいと考えています。
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様々なアスペクト比の画像が混在しているため、初めは256×256の画像にリサイズした後に、学習することを行っていました。しかし、精度があまりよくなかったため、元の画像サイズに近い、200×400に画像サイズを統一した後学習を行おうと考えています。しかし、エラーが出てしまいその原因を究明することができません。
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ネットワーク構造は基本256×256の画像の学習で使用していたものと同じです。
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以下のプログラムを参考にしています。
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https://github.com/rkuga/PSPNet/blob/master/nets/pspnet.py
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```pspnet.py
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import numpy as np
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import cv2
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import chainer
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import chainer.functions as F
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import chainer.links as L
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class PSPNet(chainer.Chain):
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# in_channel=1, out_channel=2, input_height=400, input_width =200
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def __init__(self, in_channel, out_channel, input_height, input_width):
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self.input_height = input_height
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self.input_width = input_width
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super(PSPNet, self).__init__(
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conv1_1=L.Convolution2D(in_channel, 64, 3, stride=2, pad=1, nobias=True),
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bn1_1=L.BatchNormalization(64),
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conv1_2=L.Convolution2D(64, 64, 3, stride=1, pad=1, nobias=True),
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bn1_2=L.BatchNormalization(64),
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conv1_3=L.Convolution2D(64, 128, 3, stride=1, pad=1, nobias=True),
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bn1_3=L.BatchNormalization(128),
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conv2_1_proj = L.Convolution2D(128, 256, 1, stride=1, pad=0, nobias=True),
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bn2_1_proj=L.BatchNormalization(256),
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conv2_1_res = BottleNeck_proj(128, 64, 256),
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conv2_res = Block(2, 256, 64, 256),
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conv3_1_proj = L.Convolution2D(256, 512, 1, stride=1, pad=0, nobias=True),
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bn3_1_proj=L.BatchNormalization(512),
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conv3_1_res = BottleNeck_proj(256, 128, 512),
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conv3_res = Block(3, 512, 128, 512),
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conv4_1_proj = L.Convolution2D(512, 1024, 1, stride=1, pad=0, nobias=True),
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bn4_1_proj=L.BatchNormalization(1024),
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conv4_1_res = BottleNeck_proj(512, 256, 1024),
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conv4_res = Block(7, 1024, 256, 1024, Dilate=2),
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conv4_res2 = Block(15, 1024, 256, 1024, Dilate=2),
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conv5_1_proj = L.Convolution2D(1024, 2048, 1, stride=1, pad=0, nobias=True),
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bn5_1_proj=L.BatchNormalization(2048),
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conv5_1_res = BottleNeck_proj(1024, 512, 2048),
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conv5_res = Block(2, 2048, 512, 2048, Dilate=4),
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conv5_3_pool1_conv = L.Convolution2D(2048, 512, 1, stride=1, pad=0, nobias=True),
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bn5_3_pool1_conv=L.BatchNormalization(512),
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conv5_3_pool2_conv = L.Convolution2D(2048, 512, 1, stride=1, pad=0, nobias=True),
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bn5_3_pool2_conv=L.BatchNormalization(512),
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conv5_3_pool3_conv = L.Convolution2D(2048, 512, 1, stride=1, pad=0, nobias=True),
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bn5_3_pool3_conv=L.BatchNormalization(512),
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conv5_3_pool6_conv = L.Convolution2D(2048, 512, 1, stride=1, pad=0, nobias=True),
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bn5_3_pool6_conv=L.BatchNormalization(512),
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conv5_4 = L.Convolution2D(4096, 512, 3, stride=1, pad=1, nobias=True),
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bn5_4=L.BatchNormalization(512),
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conv6 = L.Convolution2D(512, out_channel, 1, stride=1, pad=0, nobias=True),
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def __call__(self, x):
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conv1_1 = F.relu(self.bn1_1(self.conv1_1(x)))
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conv1_2 = F.relu(self.bn1_2(self.conv1_2(conv1_1)))
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conv1_3 = F.relu(self.bn1_3(self.conv1_3(conv1_2)))
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pool1 = F.max_pooling_2d(conv1_3,3,stride=2)
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conv2_1_proj = self.bn2_1_proj(self.conv2_1_proj(pool1))
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conv2_1 = self.conv2_1_res(pool1, conv2_1_proj)
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conv2_3 = self.conv2_res(conv2_1)
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conv3_1_proj = self.bn3_1_proj(self.conv3_1_proj(conv2_3))
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conv3_1 = self.conv3_1_res(conv2_3, conv3_1_proj)
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conv3_4 = self.conv3_res(conv3_1)
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conv4_1_proj = self.bn4_1_proj(self.conv4_1_proj(conv3_4))
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conv4_1 = self.conv4_1_res(conv3_4, conv4_1_proj)
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conv4_23_1 = self.conv4_res(conv4_1)
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conv4_23 = self.conv4_res2(conv4_23_1)
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conv5_1_proj = self.bn5_1_proj(self.conv5_1_proj(conv4_23))
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conv5_1 = self.conv5_1_res(conv4_23, conv5_1_proj)
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conv5_3 = self.conv5_res(conv5_1)
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_,c,h,w = conv5_3.data.shape
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conv5_3_pool1 = F.average_pooling_2d(conv5_3, h, stride=h)
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print(conv5_3_pool1.data.shape)
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conv5_3_pool1_conv = self.bn5_3_pool1_conv(self.conv5_3_pool1_conv(conv5_3_pool1))
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conv5_3_pool1_interp = F.resize_images(conv5_3_pool1_conv, (h,w))
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conv5_3_pool2 = F.average_pooling_2d(conv5_3, int(h/2), stride=int(h/2))
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conv5_3_pool2_conv = self.bn5_3_pool2_conv(self.conv5_3_pool2_conv(conv5_3_pool2))
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conv5_3_pool2_interp = F.resize_images(conv5_3_pool2_conv, (h,w))
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conv5_3_pool3 = F.average_pooling_2d(conv5_3, int(h/3), stride=int(h/3))
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conv5_3_pool3_conv = self.bn5_3_pool3_conv(self.conv5_3_pool3_conv(conv5_3_pool3))
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conv5_3_pool3_interp = F.resize_images(conv5_3_pool3_conv, (h,w))
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conv5_3_pool6 = F.average_pooling_2d(conv5_3, int(h/6), stride=int(h/6))
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conv5_3_pool6_conv = self.bn5_3_pool6_conv(self.conv5_3_pool6_conv(conv5_3_pool6))
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conv5_3_pool6_interp = F.resize_images(conv5_3_pool6_conv, (h,w))
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conv5_3_concat = F.concat((conv5_3, conv5_3_pool6_interp))
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conv5_3_concat = F.concat((conv5_3_concat, conv5_3_pool3_interp))
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conv5_3_concat = F.concat((conv5_3_concat, conv5_3_pool2_interp))
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conv5_3_concat = F.concat((conv5_3_concat, conv5_3_pool1_interp))
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conv5_4 = F.relu(self.bn5_4(self.conv5_4(conv5_3_concat)))
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conv5_4 = F.dropout(conv5_4, ratio=.1)
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conv6 = self.conv6(conv5_4)
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conv6_interp = F.resize_images(conv6, (self.input_height,self.input_width))
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return conv6_interp
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```
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以下エラー内容です。
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`assert y_w > 0, 'Width in the output should be positive.'`から、average_pooling_2dにおいて幅が負の値になっているということのようですが、その理由が分かりません。どこを改善すればよいのでしょうか?わかる方がいましたら教えていただけると幸いです。よろしくお願いいたします。
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```
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C:\Users\hoge\Anaconda3\lib\site-packages\chainer\functions\normalization\batch_normalization.py:67: UserWarning: A batch with no more than one sample has been given to F.batch_normalization. F.batch_normalization will always output a zero tensor for such batches. This could be caused by incorrect configuration in your code (such as running evaluation while chainer.config.train=True), but could also happen in the last batch of training if non-repeating iterator is used.
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UserWarning)
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Traceback (most recent call last):
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File "PSPtrain.py", line 63, in main
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z = net(x)
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File "C:\Users\hoge\pspnet.py", line 166, in __call__
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conv5_3_pool2 = F.average_pooling_2d(conv5_3, int(h/2), stride=int(h/2))
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File "C:\Users\hoge\Anaconda3\lib\site-packages\chainer\functions\pooling\average_pooling_2d.py", line 214, in average_pooling_2d
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return AveragePooling2D(ksize, stride, pad, False).apply((x,))[0]
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File "C:\Users\hoge\Anaconda3\lib\site-packages\chainer\function_node.py", line 258, in apply
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outputs = self.forward(in_data)
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File "C:\Users\hoge\Anaconda3\lib\site-packages\chainer\function_node.py", line 367, in forward
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return self.forward_gpu(inputs)
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File "C:\Users\hoge\Anaconda3\lib\site-packages\chainer\functions\pooling\average_pooling_2d.py", line 56, in forward_gpu
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return super(AveragePooling2D, self).forward_gpu(x)
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File "C:\Users\hoge\Anaconda3\lib\site-packages\chainer\functions\pooling\pooling_2d.py", line 55, in forward_gpu
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assert y_w > 0, 'Width in the output should be positive.'
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AssertionError: Width in the output should be positive.
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```
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