# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """ Test Darknet Models =================== This article is a test script to test darknet models with Relay. All the required models and libraries will be downloaded from the internet by the script. """ import numpy as np import tvm from tvm.contrib import graph_runtime from tvm.contrib.download import download_testdata download_testdata.__test__ = False from tvm.relay.testing.darknet import LAYERTYPE from tvm.relay.testing.darknet import __darknetffi__ from tvm.relay.frontend.darknet import ACTIVATION from tvm import relay REPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/' DARKNET_LIB = 'libdarknet2.0.so' DARKNETLIB_URL = REPO_URL + 'lib/' + DARKNET_LIB + '?raw=true' LIB = __darknetffi__.dlopen(download_testdata(DARKNETLIB_URL, DARKNET_LIB, module='darknet')) DARKNET_TEST_IMAGE_NAME = 'dog.jpg' DARKNET_TEST_IMAGE_URL = REPO_URL + 'data/' + DARKNET_TEST_IMAGE_NAME +'?raw=true' DARKNET_TEST_IMAGE_PATH = download_testdata(DARKNET_TEST_IMAGE_URL, DARKNET_TEST_IMAGE_NAME, module='data') def _read_memory_buffer(shape, data, dtype='float32'): length = 1 for x in shape: length *= x data_np = np.zeros(length, dtype=dtype) for i in range(length): data_np[i] = data[i] return data_np.reshape(shape) def _get_tvm_output(net, data, build_dtype='float32', states=None): '''Compute TVM output''' dtype = 'float32' mod, params = relay.frontend.from_darknet(net, data.shape, dtype) target = 'llvm' shape_dict = {'data': data.shape} graph, library, params = relay.build(mod, target, params=params) # Execute on TVM ctx = tvm.cpu(0) m = graph_runtime.create(graph, library, ctx) # set inputs m.set_input('data', tvm.nd.array(data.astype(dtype))) if states: for name in states.keys(): m.set_input(name, tvm.nd.array(states[name].astype(dtype))) m.set_input(**params) m.run() # get outputs tvm_out = [] for i in range(m.get_num_outputs()): tvm_out.append(m.get_output(i).asnumpy()) return tvm_out def _load_net(cfg_url, cfg_name, weights_url, weights_name): cfg_path = download_testdata(cfg_url, cfg_name, module='darknet') weights_path = download_testdata(weights_url, weights_name, module='darknet') net = LIB.load_network(cfg_path.encode('utf-8'), weights_path.encode('utf-8'), 0) return net def verify_darknet_frontend(net, build_dtype='float32'): '''Test network with given input image on both darknet and tvm''' def get_darknet_output(net, img): LIB.network_predict_image(net, img) out = [] for i in range(net.n): layer = net.layers[i] if layer.type == LAYERTYPE.REGION: attributes = np.array([layer.n, layer.out_c, layer.out_h, layer.out_w, layer.classes, layer.coords, layer.background], dtype=np.int32) out.insert(0, attributes) out.insert(0, _read_memory_buffer((layer.n*2, ), layer.biases)) layer_outshape = (layer.batch, layer.out_c, layer.out_h, layer.out_w) out.insert(0, _read_memory_buffer(layer_outshape, layer.output)) elif layer.type == LAYERTYPE.YOLO: attributes = np.array([layer.n, layer.out_c, layer.out_h, layer.out_w, layer.classes, layer.total], dtype=np.int32) out.insert(0, attributes) out.insert(0, _read_memory_buffer((layer.total*2, ), layer.biases)) out.insert(0, _read_memory_buffer((layer.n, ), layer.mask, dtype='int32')) layer_outshape = (layer.batch, layer.out_c, layer.out_h, layer.out_w) out.insert(0, _read_memory_buffer(layer_outshape, layer.output)) elif i == net.n-1: if layer.type == LAYERTYPE.CONNECTED: darknet_outshape = (layer.batch, layer.out_c) elif layer.type in [LAYERTYPE.SOFTMAX]: darknet_outshape = (layer.batch, layer.outputs) else: darknet_outshape = (layer.batch, layer.out_c, layer.out_h, layer.out_w) out.insert(0, _read_memory_buffer(darknet_outshape, layer.output)) return out dtype = 'float32' img = LIB.letterbox_image(LIB.load_image_color(DARKNET_TEST_IMAGE_PATH.encode('utf-8'), 0, 0), net.w, net.h) darknet_output = get_darknet_output(net, img) batch_size = 1 data = np.empty([batch_size, img.c, img.h, img.w], dtype) i = 0 for c in range(img.c): for h in range(img.h): for k in range(img.w): data[0][c][h][k] = img.data[i] i = i + 1 tvm_out = _get_tvm_output(net, data, build_dtype) for tvm_outs, darknet_out in zip(tvm_out, darknet_output): tvm.testing.assert_allclose(darknet_out, tvm_outs, rtol=1e-3, atol=1e-3) def _test_rnn_network(net, states): '''Test network with given input data on both darknet and tvm''' def get_darknet_network_predict(net, data): return LIB.network_predict(net, data) from cffi import FFI ffi = FFI() np_arr = np.zeros([1, net.inputs], dtype='float32') np_arr[0, 2] = 1 cffi_arr = ffi.cast('float*', np_arr.ctypes.data) tvm_out = _get_tvm_output(net, np_arr, states=states)[0] darknet_output = get_darknet_network_predict(net, cffi_arr) darknet_out = np.zeros(net.outputs, dtype='float32') for i in range(net.outputs): darknet_out[i] = darknet_output[i] last_layer = net.layers[net.n-1] darknet_outshape = (last_layer.batch, last_layer.outputs) darknet_out = darknet_out.reshape(darknet_outshape) tvm.testing.assert_allclose(darknet_out, tvm_out, rtol=1e-4, atol=1e-4) def test_forward_extraction(): '''test extraction model''' model_name = 'extraction' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_alexnet(): '''test alexnet model''' model_name = 'alexnet' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_resnet50(): '''test resnet50 model''' model_name = 'resnet50' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_resnext50(): '''test resnet50 model''' model_name = 'resnext50' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_yolov2(): '''test yolov2 model''' model_name = 'yolov2' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) build_dtype = {} verify_darknet_frontend(net, build_dtype) LIB.free_network(net) def test_forward_yolov3(): '''test yolov3 model''' model_name = 'yolov3' cfg_name = model_name + '.cfg' weights_name = model_name + '.weights' cfg_url = 'https://github.com/pjreddie/darknet/blob/master/cfg/' + cfg_name + '?raw=true' weights_url = 'http://pjreddie.com/media/files/' + weights_name + '?raw=true' net = _load_net(cfg_url, cfg_name, weights_url, weights_name) build_dtype = {} verify_darknet_frontend(net, build_dtype) LIB.free_network(net) def test_forward_convolutional(): '''test convolutional layer''' net = LIB.make_network(1) layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0) net.layers[0] = layer net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_dense(): '''test fully connected layer''' net = LIB.make_network(1) layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0) net.layers[0] = layer net.w = net.h = 5 LIB.resize_network(net, 5, 5) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_dense_batchnorm(): '''test fully connected layer with batchnorm''' net = LIB.make_network(1) layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0) for i in range(5): layer.rolling_mean[i] = np.random.rand(1) layer.rolling_variance[i] = np.random.rand(1) + 0.5 layer.scales[i] = np.random.rand(1) net.layers[0] = layer net.w = net.h = 2 LIB.resize_network(net, 2, 2) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_maxpooling(): '''test maxpooling layer''' net = LIB.make_network(1) layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0) net.layers[0] = layer net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_avgpooling(): '''test avgerage pooling layer''' net = LIB.make_network(1) layer = LIB.make_avgpool_layer(1, 224, 224, 3) net.layers[0] = layer net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_conv_batch_norm(): '''test batch normalization layer''' net = LIB.make_network(1) layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1, 0, 0, 0) for i in range(32): layer.rolling_mean[i] = np.random.rand(1) layer.rolling_variance[i] = np.random.rand(1) + 0.5 net.layers[0] = layer net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_shortcut(): '''test shortcut layer''' net = LIB.make_network(3) layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0) layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1, 0, 0, 0, 0) layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32) layer_3.activation = ACTIVATION.RELU layer_3.alpha = 1 layer_3.beta = 1 net.layers[0] = layer_1 net.layers[1] = layer_2 net.layers[2] = layer_3 net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_reorg(): '''test reorg layer''' net = LIB.make_network(2) layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0) layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0) net.layers[0] = layer_1 net.layers[1] = layer_2 net.w = net.h = 222 LIB.resize_network(net, 222, 222) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_region(): '''test region layer''' net = LIB.make_network(2) layer_1 = LIB.make_convolutional_layer(1, 19, 19, 3, 425, 1, 1, 1, 0, 1, 0, 0, 0, 0) layer_2 = LIB.make_region_layer(1, 19, 19, 5, 80, 4) layer_2.softmax = 1 net.layers[0] = layer_1 net.layers[1] = layer_2 net.w = net.h = 19 LIB.resize_network(net, 19, 19) build_dtype = {} verify_darknet_frontend(net, build_dtype) LIB.free_network(net) def test_forward_yolo_op(): '''test yolo layer''' net = LIB.make_network(2) layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 14, 1, 3, 2, 0, 1, 0, 0, 0, 0) layer_2 = LIB.make_yolo_layer(1, 111, 111, 2, 9, __darknetffi__.NULL, 2) net.layers[0] = layer_1 net.layers[1] = layer_2 net.w = net.h = 224 LIB.resize_network(net, 224, 224) build_dtype = {} verify_darknet_frontend(net, build_dtype) LIB.free_network(net) def test_forward_upsample(): '''test upsample layer''' net = LIB.make_network(1) layer = LIB.make_upsample_layer(1, 19, 19, 3, 3) layer.scale = 1 net.layers[0] = layer net.w = net.h = 19 LIB.resize_network(net, 19, 19) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_l2normalize(): '''test l2 normalization layer''' net = LIB.make_network(1) layer = LIB.make_l2norm_layer(1, 224*224*3) layer.c = layer.out_c = 3 layer.h = layer.out_h = 224 layer.w = layer.out_w = 224 net.layers[0] = layer net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_elu(): '''test elu activation layer''' net = LIB.make_network(1) layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0, 0, 0, 0) layer_1.activation = ACTIVATION.ELU net.layers[0] = layer_1 net.w = net.h = 224 LIB.resize_network(net, 224, 224) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_softmax(): '''test softmax layer''' net = LIB.make_network(1) layer_1 = LIB.make_softmax_layer(1, 75, 1) layer_1.temperature = 1 net.layers[0] = layer_1 net.w = net.h = 5 LIB.resize_network(net, net.w, net.h) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_softmax_temperature(): '''test softmax layer''' net = LIB.make_network(1) layer_1 = LIB.make_softmax_layer(1, 75, 1) layer_1.temperature = 0.8 net.layers[0] = layer_1 net.w = net.h = 5 LIB.resize_network(net, net.w, net.h) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_activation_logistic(): '''test logistic activation layer''' net = LIB.make_network(1) batch = 1 h = 224 w = 224 c = 3 n = 32 groups = 1 size = 3 stride = 2 padding = 0 activation = ACTIVATION.LOGISTIC batch_normalize = 0 binary = 0 xnor = 0 adam = 0 layer_1 = LIB.make_convolutional_layer(batch, h, w, c, n, groups, size, stride, padding, activation, batch_normalize, binary, xnor, adam) net.layers[0] = layer_1 net.w = w net.h = h LIB.resize_network(net, net.w, net.h) verify_darknet_frontend(net) LIB.free_network(net) def test_forward_rnn(): '''test RNN layer''' net = LIB.make_network(1) batch = 1 inputs = 4 outputs = 4 steps = 1 activation = ACTIVATION.RELU batch_normalize = 0 adam = 0 layer_1 = LIB.make_rnn_layer(batch, inputs, outputs, steps, activation, batch_normalize, adam) net.layers[0] = layer_1 net.inputs = inputs net.outputs = outputs net.w = net.h = 0 LIB.resize_network(net, net.w, net.h) states = {"rnn0_state": np.zeros([1, net.inputs])} _test_rnn_network(net, states) LIB.free_network(net) if __name__ == '__main__': test_forward_resnet50() test_forward_resnext50() test_forward_alexnet() test_forward_extraction() test_forward_yolov2() test_forward_yolov3() test_forward_convolutional() test_forward_maxpooling() test_forward_avgpooling() test_forward_conv_batch_norm() test_forward_shortcut() test_forward_dense() test_forward_dense_batchnorm() test_forward_softmax() test_forward_softmax_temperature() test_forward_reorg() test_forward_region() test_forward_yolo_op() test_forward_upsample() test_forward_l2normalize() test_forward_elu() test_forward_rnn() test_forward_activation_logistic()