TinynetUnpooling.py 文件源码

def __call__(self, input_blob, test_mode=False):
        # explicit and very flexible DAG!
        #################################
        data = input_blob[0]
        labels = input_blob[1]

        if(len(input_blob) >= 3):
            weights_classes = input_blob[2]
        else:
            weights_classes = chainer.Variable(cuda.cupy.ones((self.classes, 1), dtype='float32'))

        # ---- CONTRACTION BLOCKS ---- #
        blob_b0  = self.bnorm0(data)
        (blob_b1, indices_b1, size_b1)  = F.max_pooling_2dIndices(self.bnorm1(F.relu(self.conv1(blob_b0)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b2, indices_b2, size_b2)  = F.max_pooling_2dIndices(self.bnorm2(F.relu(self.conv2(blob_b1)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b3, indices_b3, size_b3)  = F.max_pooling_2dIndices(self.bnorm3(F.relu(self.conv3(blob_b2)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))
        (blob_b4, indices_b4, size_b4)  = F.max_pooling_2dIndices(self.bnorm4(F.relu(self.conv4(blob_b3)), test=test_mode), (2, 2), stride=(2,2), pad=(0, 0))

        # ---- EXPANSION BLOCKS ---- #
        blob_b5  = self.bnorm5(F.relu(self.conv5(F.unpooling_2d(blob_b4, indices_b4, size_b4))), test=test_mode)
        blob_b6  = self.bnorm6(F.relu(self.conv6(F.unpooling_2d(blob_b5, indices_b3, size_b3))), test=test_mode)
        blob_b7  = self.bnorm7(F.relu(self.conv7(F.unpooling_2d(blob_b6, indices_b2, size_b2))), test=test_mode)
        blob_b8  = self.bnorm8(F.relu(self.conv8(F.unpooling_2d(blob_b7, indices_b1, size_b1))), test=test_mode)

        #ipdb.set_trace()

        # ---- SOFTMAX CLASSIFIER ---- #
        self.blob_class = self.classi(blob_b8)
        self.probs = F.softmax(self.blob_class)

        # ---- CROSS-ENTROPY LOSS ---- #
        #ipdb.set_trace()
            self.loss = F.weighted_cross_entropy(self.probs, labels, weights_classes, normalize=True)
        self.output_point = self.probs

        return self.loss