python类to_proto()的实例源码

def test_caffe_export(self):
        data = L.Input(shape={'dim': [10, 3, 16, 224, 224]})
        top = L.Convolution(data, kernel_size=3, pad=1, stride=1, num_output=128, dilation=1,
                            weight_filler={'type': 'xavier'}, bias_filler={'type': 'constant'})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        response['net']['l0']['params']['caffe'] = True
        response['net']['l1']['params']['layer_type'] = '3D'
        response['net']['l1']['params']['caffe'] = False
        response = self.client.post(reverse('caffe-export'), {'net': json.dumps(response['net']),
                                                              'net_name': ''})
        response = json.loads(response.content)
        self.assertEqual(response['result'], 'error')


# ********** Data Layers Test **********

def test_caffe_import(self):
        data, label = L.WindowData(source='/dummy/source/', batch_size=32, ntop=2,
                                   fg_threshold=0.5, bg_threshold=0.5, fg_fraction=0.25,
                                   context_pad=0, crop_mode='warp', cache_images=False,
                                   root_folder='/dummy/folder/',
                                   transform_param=dict(crop_size=227, mean_value=[104, 117, 123],
                                                        mirror=True, force_color=False,
                                                        force_gray=False))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 14)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        # Test 1
        top = L.Pooling(kernel_size=2, pad=0, stride=2, pool=1)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 4)
        self.assertEqual(response['result'], 'success')
        # Test 2
        top = L.Pooling(kernel_size=2, pad=0, stride=2, pool=2)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 4)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        # Test 1
        data = L.Input(shape={'dim': [10, 3, 224, 224]})
        top = L.Python(data, module='pyloss', layer='EuclideanLossLayer', loss_weight=1, name='eucLoss')
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l1']['params']), 4)
        self.assertEqual(response['result'], 'success')
        # Test 2
        top = L.Python(module='pascal_multilabel_datalayers', layer='PascalMultilabelDataLayerSync',
                       param_str="{\'pascal_root\': \'../data/pascal/VOC2007\', \'im_shape\': [227, 227], \
                        \'split\': \'train\', \'batch_size\': 128}")
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 6)
        self.assertEqual(response['result'], 'success')

def densenet(data_file, mode='train', batch_size=64, depth=40, first_output=16, growth_rate=12, dropout=0.2):
    data, label = L.Data(source=data_file, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, 
              transform_param=dict(mean_file="/home/zl499/caffe/examples/cifar10/mean.binaryproto"))

    nchannels = first_output
    model = L.Convolution(data, kernel_size=3, stride=1, num_output=nchannels,
                        pad=1, bias_term=False, weight_filler=dict(type='msra'), bias_filler=dict(type='constant'))

    N = (depth-4)/3
    for i in range(N):
        model = add_layer(model, growth_rate, dropout)
        nchannels += growth_rate
    model = transition(model, nchannels, dropout)

    for i in range(N):
        model = add_layer(model, growth_rate, dropout)
        nchannels += growth_rate
    model = transition(model, nchannels, dropout)

    for i in range(N):
        model = add_layer(model, growth_rate, dropout)
        nchannels += growth_rate


    model = L.BatchNorm(model, in_place=False, param=[dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)])
    model = L.Scale(model, bias_term=True, in_place=True, filler=dict(value=1), bias_filler=dict(value=0))
    model = L.ReLU(model, in_place=True)
    model = L.Pooling(model, pool=P.Pooling.AVE, global_pooling=True)
    model = L.InnerProduct(model, num_output=10, bias_term=True, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant'))
    loss = L.SoftmaxWithLoss(model, label)
    accuracy = L.Accuracy(model, label)
    return to_proto(loss, accuracy)

def caffenet(lmdb, batch_size=256, include_acc=False):
    data, label = L.Data(source=lmdb, backend=P.Data.LMDB,
                         batch_size=batch_size, ntop=2)

    # the net itself
    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
    pool1 = max_pool(relu1, 3, stride=2)
    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
    pool2 = max_pool(relu2, 3, stride=2)
    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
    pool5 = max_pool(relu5, 3, stride=2)
    fc6, relu6 = fc_relu(pool5, 4096)
    drop6 = L.Dropout(relu6, in_place=True)
    fc7, relu7 = fc_relu(drop6, 4096)
    drop7 = L.Dropout(relu7, in_place=True)
    fc8 = L.InnerProduct(drop7, num_output=1000)
    loss = L.SoftmaxWithLoss(fc8, label)

    if include_acc:
        acc = L.Accuracy(fc8, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)

def convert(self, net_string):
        net_list = cnn.parse('net', net_string)
        net_list = StateStringUtils(self.ssp).convert_model_string_to_states(net_list)[1:]
        data, label = self.create_top_layer(caffe.TRAIN, self.hp.TRAIN_FILE, train=True)
        data1, label1 = self.create_top_layer(caffe.TEST, self.hp.VAL_FILE, train=False)
        loss, acc = self.unpack_list(net_list, data, label)
        lls = [data, data1, acc, loss]

        cc = to_proto(*lls)
        cc = self.replace_top_names(cc)
        return cc

    # Iterate over token list from parser.

def test_caffe_export(self):
        data = L.Input(shape={'dim': [10, 3, 224, 224]})
        top = L.Convolution(data, kernel_size=3, pad=1, stride=1, num_output=128, dilation=1,
                            weight_filler={'type': 'xavier'}, bias_filler={'type': 'constant'})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        response['net']['l0']['params']['caffe'] = True
        response['net']['l1']['params']['caffe'] = True
        response = self.client.post(reverse('caffe-export'), {'net': json.dumps(response['net']),
                                                              'net_name': ''})
        response = json.loads(response.content)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        # Test 1
        data, label = L.ImageData(source='/dummy/source/', batch_size=32, ntop=2, rand_skip=0,
                                  shuffle=False, new_height=256, new_width=256, is_color=False,
                                  root_folder='/dummy/folder/',
                                  transform_param=dict(crop_size=227, mean_value=[104, 117, 123],
                                                       mirror=True, force_color=False,
                                                       force_gray=False))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 13)
        self.assertEqual(response['result'], 'success')
        # Test 2
        data, label = L.ImageData(source='/dummy/source/', batch_size=32, ntop=2, rand_skip=0,
                                  shuffle=False, new_height=256, new_width=256, is_color=False,
                                  root_folder='/dummy/folder/', include=dict(phase=caffe.TRAIN),
                                  transform_param=dict(crop_size=227, mean_file='/path/to/file',
                                                       mirror=True, force_color=False,
                                                       force_gray=False))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 13)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        # Test 1
        data, label = L.Data(source='/dummy/source/', backend=P.Data.LMDB, batch_size=32, ntop=2,
                             rand_skip=0, prefetch=10,
                             transform_param=dict(crop_size=227, mean_value=[104, 117, 123],
                                                  mirror=True, force_color=False,
                                                  force_gray=False))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 10)
        self.assertEqual(response['result'], 'success')
        # Test 2
        data, label = L.Data(source='/dummy/source/', backend=P.Data.LEVELDB, batch_size=32, ntop=2,
                             rand_skip=0, prefetch=10,
                             transform_param=dict(crop_size=227, mean_value=[104, 117, 123],
                                                  mirror=True, force_color=False,
                                                  force_gray=False))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 10)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.HDF5Output(file_name='/dummy/filename')
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        data = L.Input(shape={'dim': [10, 3, 224, 224]})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        data, label = L.MemoryData(batch_size=32, ntop=2, channels=3, height=224, width=224)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data, label)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 4)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        data = L.DummyData(shape={'dim': [10, 3, 224, 224]},
                           data_filler={'type': 'constant'})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(data)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')


# ********** Vision Layers Test **********

def test_caffe_import(self):
        top = L.SPP(pyramid_height=2, pool=1)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 2)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.Crop(axis=2, offset=2)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 2)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        # Test 1
        top = L.Deconvolution(convolution_param=dict(kernel_size=3, pad=1, stride=1, num_output=128,
                              weight_filler={'type': 'xavier'}, bias_filler={'type': 'constant'}))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 6)
        self.assertEqual(response['result'], 'success')
        # Test 2
        top = L.Deconvolution(convolution_param=dict(kernel_w=3, kernel_h=3, pad_w=1, pad_h=1, stride=1,
                              num_output=128, dilation=1, weight_filler={'type': 'xavier'},
                              bias_filler={'type': 'constant'}))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 6)
        self.assertEqual(response['result'], 'success')


# ********** Recurrent Layers Test **********

def test_caffe_import(self):
        top = L.Recurrent(recurrent_param=dict(num_output=128, debug_info=False,
                          expose_hidden=False, weight_filler={'type': 'xavier'},
                          bias_filler={'type': 'constant'}))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 5)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.LSTM(recurrent_param=dict(num_output=128, debug_info=False,
                     expose_hidden=False, weight_filler={'type': 'xavier'},
                     bias_filler={'type': 'constant'}))
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 5)
        self.assertEqual(response['result'], 'success')


# ********** Common Layers Test **********

def test_caffe_import(self):
        top = L.InnerProduct(num_output=128, weight_filler={'type': 'xavier'},
                             bias_filler={'type': 'constant'})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 3)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.Dropout()
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.Embed(num_output=128, input_dim=2, bias_term=False,
                      weight_filler={'type': 'xavier'})
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 4)
        self.assertEqual(response['result'], 'success')


# ********** Normalisation Layers Test **********

def test_caffe_import(self):
        top = L.LRN(local_size=5, alpha=1, beta=0.75, k=1, norm_region=1, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 5)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.BatchNorm(use_global_stats=True, moving_average_fraction=0.999, eps=1e-5, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 3)
        self.assertEqual(response['result'], 'success')


# ********** Activation / Neuron Layers Test **********

def test_caffe_import(self):
        top = L.ReLU(negative_slope=0, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.PReLU(channel_shared=False, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.ELU(alpha=1, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 1)
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.Sigmoid(in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.AbsVal(in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertEqual(response['result'], 'success')

def test_caffe_import(self):
        top = L.Power(power=1.0, scale=1.0, shift=0.0, in_place=True)
        with open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'w') as f:
            f.write(str(to_proto(top)))
        sample_file = open(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'), 'r')
        response = self.client.post(reverse('caffe-import'), {'file': sample_file})
        response = json.loads(response.content)
        os.remove(os.path.join(settings.BASE_DIR, 'media', 'test.prototxt'))
        self.assertGreaterEqual(len(response['net']['l0']['params']), 3)
        self.assertEqual(response['result'], 'success')