def test_forward_where(): cond = mx.sym.var('cond') x = mx.sym.var('x') y = mx.sym.var('y') dshape = (2, 2) dtype = 'float32' mx_sym = mx.sym.where(cond, x, y) np_cond = np.array([[0, 1], [-1, 0]]).astype(dtype) np_x = np.random.uniform(size=dshape).astype(dtype) np_y = np.random.uniform(size=dshape).astype(dtype) mx_cond = mx.nd.array(np_cond) mx_x = mx.nd.array(np_x) mx_y = mx.nd.array(np_y) mod = mx.mod.Module(mx_sym, label_names=None, data_names=['cond', 'x', 'y']) mod.bind(data_shapes=[('cond', dshape), ('x', dshape), ('y', dshape)], for_training=False) mod.init_params() args, auxs = mod.get_params() mx_out = mx.nd.where(mx_cond, mx_x, mx_y).asnumpy() out_shape = dshape new_sym, params = frontend.from_mxnet(mx_sym, args, auxs) shape_dict = {'cond': dshape, 'x': dshape, 'y': dshape} for target, ctx in ctx_list(): with nnvm.compiler.build_config(opt_level=3): graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params) m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input("cond", tvm.nd.array(np_cond)) m.set_input("x", tvm.nd.array(np_x)) m.set_input("y", tvm.nd.array(np_y)) m.set_input(**params) m.run() # get outputs tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy() tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
def test_forward_minimum(): a = mx.sym.var('a') b = mx.sym.var('b') dshape = (10, 20) dtype = 'float32' mx_sym = mx.sym._internal._minimum(a, b) np_a = np.random.uniform(size=dshape).astype(dtype) np_b = np.random.uniform(size=dshape).astype(dtype) mx_a = mx.nd.array(np_a) mx_b = mx.nd.array(np_b) mod = mx.mod.Module(mx_sym, label_names=None, data_names=['a', 'b']) mod.bind(data_shapes=[('a', dshape), ('b', dshape)], for_training=False) mod.init_params() args, auxs = mod.get_params() mx_out = mx.nd._internal._minimum(mx_a, mx_b).asnumpy() out_shape = dshape new_sym, params = frontend.from_mxnet(mx_sym, args, auxs) shape_dict = {'a': dshape, 'b': dshape} for target, ctx in ctx_list(): with nnvm.compiler.build_config(opt_level=3): graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params) m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input("a", tvm.nd.array(np_a)) m.set_input("b", tvm.nd.array(np_b)) m.set_input(**params) m.run() # get outputs tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy() tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
def get_tvm_output(symbol, x, args, auxs, target, ctx, dtype='float32'): if gluon_impl: new_sym, params = frontend.from_mxnet(symbol) else: new_sym, params = frontend.from_mxnet(symbol, args, auxs) dshape = x.shape shape_dict = {'data': dshape} with nnvm.compiler.build_config(opt_level=3): graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params) m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input("data", tvm.nd.array(x.astype(dtype))) m.set_input(**params) m.run() # get outputs out = m.get_output(0, tvm.nd.empty(out_shape, dtype)) return out.asnumpy()
def get_tvm_workload(network, **kwargs): from nnvm.frontend import from_mxnet from mxnet.gluon.model_zoo.vision import get_model block = get_model(network, **kwargs) if network.startswith('resnet152'): import sys sys.setrecursionlimit(10000) sym, params = from_mxnet(block) sym = nnvm.sym.softmax(sym) return sym, params
_, arg_params, aux_params = load_checkpoint("%s/%s" % (dir, model_name), 0) import argparse parser = argparse.ArgumentParser() parser.add_argument( "-f", "--frontend", help="Frontend for compilation, nnvm or relay", type=str, default="nnvm") args = parser.parse_args() if args.frontend == "relay": net, params = relay.frontend.from_mxnet(sym, {"data": dshape}, arg_params=arg_params, aux_params=aux_params) with relay.build_config(opt_level=3): graph, lib, params = relay.build(net, target, params=params) elif args.frontend == "nnvm": net, params = from_mxnet(sym, arg_params, aux_params) with compiler.build_config(opt_level=3): graph, lib, params = compiler.build( net, target, {"data": dshape}, params=params) else: parser.print_help() parser.exit() ###################################################################### # Create TVM runtime and do inference # Preprocess image image = cv2.imread(test_image_path) img_data = cv2.resize(image, (dshape[2], dshape[3])) img_data = img_data[:, :, (2, 1, 0)].astype(np.float32) img_data -= np.array([123, 117, 104])
download(image_url, test_image_path) download(inference_symbol_url, inference_symbol_path) zip_ref = zipfile.ZipFile(model_file_path, 'r') zip_ref.extractall(dir) zip_ref.close() zip_ref = zipfile.ZipFile(inference_symbol_path) zip_ref.extractall(dir) zip_ref.close() ###################################################################### # Convert and compile model with NNVM for CPU. sym = mx.sym.load("%s/%s/ssd_resnet50_inference.json" % (dir, inference_symbol_folder)) _, arg_params, aux_params = load_checkpoint("%s/%s" % (dir, model_name), 0) net, params = from_mxnet(sym, arg_params, aux_params) with compiler.build_config(opt_level=3): graph, lib, params = compiler.build(net, target, {"data": dshape}, params=params) ###################################################################### # Create TVM runtime and do inference # Preprocess image image = cv2.imread(test_image_path) img_data = cv2.resize(image, (dshape[2], dshape[3])) img_data = img_data[:, :, (2, 1, 0)].astype(np.float32) img_data -= np.array([123, 117, 104]) img_data = np.transpose(np.array(img_data), (2, 0, 1)) img_data = np.expand_dims(img_data, axis=0) # Build TVM runtime m = graph_runtime.create(graph, lib, ctx)
def test_ssd(): model_name = "ssd_resnet50_512" model_file = "%s.zip" % model_name test_image = "dog.jpg" dshape = (1, 3, 512, 512) ###################################################################### # Download MXNet SSD pre-trained model and demo image # --------------------------------------------------- # Pre-trained model available at # https://github.com/apache/incubator-\mxnet/tree/master/example/ssd model_url = "https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/" \ "resnet50_ssd_512_voc0712_trainval.zip" image_url = "https://cloud.githubusercontent.com/assets/3307514/20012567/" \ "cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg" inference_symbol_folder = "c1904e900848df4548ce5dfb18c719c7-a28c4856c827fe766aa3da0e35bad41d44f0fb26" inference_symbol_url = "https://gist.github.com/kevinthesun/c1904e900848df4548ce5dfb18c719c7/" \ "archive/a28c4856c827fe766aa3da0e35bad41d44f0fb26.zip" dir = "ssd_model" if not os.path.exists(dir): os.makedirs(dir) model_file_path = "%s/%s" % (dir, model_file) test_image_path = "%s/%s" % (dir, test_image) inference_symbol_path = "%s/inference_model.zip" % dir download(model_url, model_file_path) download(image_url, test_image_path) download(inference_symbol_url, inference_symbol_path) zip_ref = zipfile.ZipFile(model_file_path, 'r') zip_ref.extractall(dir) zip_ref.close() zip_ref = zipfile.ZipFile(inference_symbol_path) zip_ref.extractall(dir) zip_ref.close() ###################################################################### # Convert and compile model with NNVM for CPU. sym = mx.sym.load("%s/%s/ssd_resnet50_inference.json" % (dir, inference_symbol_folder)) _, arg_params, aux_params = load_checkpoint( "%s/%s" % (dir, model_name), 0) net, params = from_mxnet(sym, arg_params, aux_params) shape_dict = {"data": dshape} with nnvm.compiler.build_config(opt_level=3): image, tvm_output = heterogeneous_ssd(net, ['nms'], shape_dict, params, test_image_path) ##################################################################### # Display result class_names = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] def display(img, out, thresh=0.5): import random import matplotlib as mpl import matplotlib.pyplot as plt mpl.rcParams['figure.figsize'] = (10, 10) pens = dict() plt.clf() plt.imshow(img) for det in out: cid = int(det[0]) if cid < 0: continue score = det[1] if score < thresh: continue if cid not in pens: pens[cid] = (random.random(), random.random(), random.random()) scales = [img.shape[1], img.shape[0]] * 2 xmin, ymin, xmax, ymax = [ int(p * s) for p, s in zip(det[2:6].tolist(), scales)] rect = plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, edgecolor=pens[cid], linewidth=3) plt.gca().add_patch(rect) text = class_names[cid] plt.gca().text(xmin, ymin - 2, '{:s} {:.3f}'.format(text, score), bbox=dict(facecolor=pens[cid], alpha=0.5), fontsize=12, color='white') plt.show() image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) display(image, tvm_output.asnumpy()[0], thresh=0.45)