我们从Python开源项目中,提取了以下50个代码示例,用于说明如何使用caffe.to_proto()。
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')
def test_caffe_import(self): top = L.Exp(base=-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')
def test_caffe_import(self): top = L.Log(base=-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')
def test_caffe_import(self): top = L.BNLL(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.Bias(axis=1, num_axes=1, 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.Scale(bias_term=False) 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') # ********** Utility Layers Test **********
def test_caffe_import(self): top = L.Flatten(axis=1, end_axis=-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.Reshape(shape={'dim': [2, -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']), 1) self.assertEqual(response['result'], 'success')
def test_caffe_import(self): top = L.BatchReindex() 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.Concat() 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.Slice(axis=1, slice_dim=1, slice_point=[1, 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']), 3) self.assertEqual(response['result'], 'success')
def test_caffe_import(self): # Test 1 top = L.Eltwise(operation=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']), 1) self.assertEqual(response['result'], 'success')
def test_caffe_import(self): top = L.Filter() 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') # This layer is currently not supported as there is no bottom blob
def test_caffe_import(self): # Test 1 top = L.Reduction(operation=1, axis=0, coeff=1.0) 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') # Test 2 top = L.Reduction(operation=2, axis=0, coeff=1.0) 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') # Test 3 top = L.Reduction(operation=3, axis=0, coeff=1.0) 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') # Test 4 top = L.Reduction(operation=4, axis=0, coeff=1.0) 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.ArgMax(out_max_val=False, top_k=1, axis=0) 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.Softmax() 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') # ********** Loss Layers Test **********
def test_caffe_import(self): top = L.MultinomialLogisticLoss() 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.InfogainLoss(source='/dummy/source/', axis=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.SoftmaxWithLoss(softmax_param=dict(axis=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']), 1) self.assertEqual(response['result'], 'success')
def test_caffe_import(self): top = L.HingeLoss(norm=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']), 1) self.assertEqual(response['result'], 'success')
def test_caffe_import(self): top = L.SigmoidCrossEntropyLoss() 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')