我们从Python开源项目中，提取了以下24个代码示例，用于说明如何使用chainer.functions.unpooling_2d()。
def __call__(self, x, test): if self.sample=="down" or self.sample=="none" or self.sample=='none-9' or self.sample=='none-7' or self.sample=='none-5': h = self.c(x) elif self.sample=="up": h = F.unpooling_2d(x, 2, 2, 0, cover_all=False) h = self.c(h) else: print("unknown sample method %s"%self.sample) if self.bn: h = self.batchnorm(h, test=test) if self.noise: h = add_noise(h, test=test) if self.dropout: h = F.dropout(h, train=not test) if not self.activation is None: h = self.activation(h) return h
def _do_before_cal(self, x): if self.nn == 'up_unpooling': x = F.unpooling_2d(x, 2, 2, 0, cover_all=False) return x
def __call__(self, x, train=True): b, c, height, width = x.shape h = self.conv(F.unpooling_2d(x, 2, outsize=(height * 2, width * 2))) if self.activate: h = F.relu(self.bn(h, test=not train)) return h
def __call__(self, x): h = F.reshape(self.l0(x), ((x.shape[0],) + self.embed_shape)) for i in range(self.n_blocks): for j in range(self.block_size): h = F.elu(getattr(self, 'c{}'.format(i*j+j))(h)) if i < self.n_blocks - 1: h = F.unpooling_2d(h, ksize=2, stride=2, cover_all=False) return self.ln(h)
def __call__(self, x): return functions.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
def check_forward(self, x_data): x = chainer.Variable(x_data) y = functions.unpooling_2d(x, self.ksize, outsize=self.outsize, cover_all=self.cover_all) self.assertEqual(y.data.dtype, self.dtype) y_data = cuda.to_cpu(y.data) self.assertEqual(self.gy.shape, y_data.shape) for i in six.moves.range(self.N): for c in six.moves.range(self.n_channels): outsize = self.outsize or self.expected_outsize assert y_data.shape[2:] == outsize if outsize == (5, 2): expect = numpy.zeros(outsize, dtype=self.dtype) expect[:2, :] = self.x[i, c, 0, 0] expect[2:4, :] = self.x[i, c, 1, 0] elif outsize == (4, 2): expect = numpy.array([ [self.x[i, c, 0, 0], self.x[i, c, 0, 0]], [self.x[i, c, 0, 0], self.x[i, c, 0, 0]], [self.x[i, c, 1, 0], self.x[i, c, 1, 0]], [self.x[i, c, 1, 0], self.x[i, c, 1, 0]], ]) elif outsize == (3, 1): expect = numpy.array([ [self.x[i, c, 0, 0]], [self.x[i, c, 0, 0]], [self.x[i, c, 1, 0]], ]) else: raise ValueError('Unsupported outsize: {}'.format(outsize)) gradient_check.assert_allclose(expect, y_data[i, c])
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
def __init__(self, ksize, stride=None, pad=0, outsize=None, cover_all=True): self._function = "unpooling_2d" self.ksize = ksize self.stride = stride self.pad = pad self.outsize = outsize self.cover_all = cover_all
def __call__(self, x): return F.unpooling_2d(x, self.ksize, self.stride, self.pad, self.outsize, self.cover_all)
