diff --git a/model_zoo/official/cv/maskrcnn/export.py b/model_zoo/official/cv/maskrcnn/export.py new file mode 100644 index 00000000000..f55a3a6f055 --- /dev/null +++ b/model_zoo/official/cv/maskrcnn/export.py @@ -0,0 +1,47 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""export checkpoint file into air models""" +import argparse +import numpy as np + +from mindspore import Tensor, context +from mindspore.train.serialization import load_checkpoint, load_param_into_net, export + +from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50 +from src.config import config + +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='maskrcnn_export') + parser.add_argument('--ckpt_file', type=str, default='', help='maskrcnn ckpt file.') + parser.add_argument('--output_file', type=str, default='', help='maskrcnn output air name.') + args_opt = parser.parse_args() + + net = Mask_Rcnn_Resnet50(config=config) + param_dict = load_checkpoint(args_opt.ckpt_file) + load_param_into_net(net, param_dict) + net.set_train(False) + + bs = config.test_batch_size + + img = Tensor(np.zeros([bs, 3, 768, 1280], np.float16)) + img_metas = Tensor(np.zeros([bs, 4], np.float16)) + gt_bboxes = Tensor(np.zeros([bs, 128, 4], np.float16)) + gt_labels = Tensor(np.zeros([bs, 128], np.int32)) + gt_num = Tensor(np.zeros([bs, 128], np.bool)) + gt_mask = Tensor(np.zeros([bs, 128], np.bool)) + export(net, img, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask, file_name=args_opt.output_file, + file_format="AIR") diff --git a/model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py b/model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py index 2afeb269886..2bc7493fc14 100644 --- a/model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py +++ b/model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py @@ -16,13 +16,11 @@ import numpy as np import mindspore.nn as nn -from mindspore import context from mindspore.ops import operations as P from mindspore.common.tensor import Tensor from mindspore.common import dtype as mstype from mindspore.common.initializer import initializer -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) def bias_init_zeros(shape): """Bias init method.""" diff --git a/model_zoo/official/cv/maskrcnn/src/maskrcnn/proposal_generator.py b/model_zoo/official/cv/maskrcnn/src/maskrcnn/proposal_generator.py index ae4c2624c1d..3c7ae5f7d93 100644 --- a/model_zoo/official/cv/maskrcnn/src/maskrcnn/proposal_generator.py +++ b/model_zoo/official/cv/maskrcnn/src/maskrcnn/proposal_generator.py @@ -19,10 +19,6 @@ import mindspore.nn as nn import mindspore.common.dtype as mstype from mindspore.ops import operations as P from mindspore import Tensor -from mindspore import context - - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) class Proposal(nn.Cell): diff --git a/model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py b/model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py index 20d9ee1f345..6dfee8aa2e0 100644 --- a/model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py +++ b/model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py @@ -19,10 +19,6 @@ import mindspore.nn as nn from mindspore.ops import operations as P from mindspore.common.tensor import Tensor from mindspore.ops import functional as F -from mindspore import context - - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) def weight_init_ones(shape): diff --git a/model_zoo/official/cv/resnet/export.py b/model_zoo/official/cv/resnet/export.py new file mode 100644 index 00000000000..f3f836c19bd --- /dev/null +++ b/model_zoo/official/cv/resnet/export.py @@ -0,0 +1,59 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +""" +##############export checkpoint file into air and onnx models################# +python export.py +""" +import argparse +import numpy as np + +from mindspore import Tensor +from mindspore.train.serialization import load_checkpoint, load_param_into_net, export + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='resnet export') + parser.add_argument('--network_dataset', type=str, default='resnet50_cifar10', choices=['resnet50_cifar10', + 'resnet50_imagenet2012', + 'resnet101_imagenet2012', + "se-resnet50_imagenet2012"], + help='network and dataset name.') + parser.add_argument('--ckpt_file', type=str, default='', help='resnet ckpt file.') + parser.add_argument('--output_file', type=str, default='', help='resnet output air name.') + args_opt = parser.parse_args() + + if args_opt.network_dataset == 'resnet50_cifar10': + from src.config import config1 as config + from src.resnet import resnet50 as resnet + elif args_opt.network_dataset == 'resnet50_imagenet2012': + from src.config import config2 as config + from src.resnet import resnet50 as resnet + elif args_opt.network_dataset == 'resnet101_imagenet2012': + from src.config import config3 as config + from src.resnet import resnet101 as resnet + elif args_opt.network_dataset == 'se-resnet50_imagenet2012': + from src.config import config4 as config + from src.resnet import se_resnet50 as resnet + else: + raise ValueError("network and dataset is not support.") + + net = resnet(config.class_num) + + assert args_opt.ckpt_file is not None, "checkpoint_path is None." + + param_dict = load_checkpoint(args_opt.ckpt_file) + load_param_into_net(net, param_dict) + + input_arr = Tensor(np.zeros([1, 3, 224, 224], np.float32)) + export(net, input_arr, file_name=args_opt.output_file, file_format="AIR") diff --git a/model_zoo/official/cv/warpctc/export.py b/model_zoo/official/cv/warpctc/export.py new file mode 100644 index 00000000000..67880d504f4 --- /dev/null +++ b/model_zoo/official/cv/warpctc/export.py @@ -0,0 +1,43 @@ +# Copyright 2020 Huawei Technologies Co., Ltd +# +# Licensed 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. +# ============================================================================ +"""export checkpoint file into air models""" +import argparse +import numpy as np + +from mindspore import Tensor, context +from mindspore.train.serialization import load_checkpoint, load_param_into_net, export + +from src.warpctc import StackedRNN +from src.config import config + +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + +if __name__ == '__main__': + + parser = argparse.ArgumentParser(description='warpctc_export') + parser.add_argument('--ckpt_file', type=str, default='', help='warpctc ckpt file.') + parser.add_argument('--output_file', type=str, default='', help='warpctc output air name.') + args_opt = parser.parse_args() + captcha_width = config.captcha_width + captcha_height = config.captcha_height + batch_size = config.batch_size + hidden_size = config.hidden_size + net = StackedRNN(captcha_height * 3, batch_size, hidden_size) + param_dict = load_checkpoint(args_opt.ckpt_file) + load_param_into_net(net, param_dict) + net.set_train(False) + + image = Tensor(np.zeros([batch_size, 3, captcha_height, captcha_width], np.float16)) + export(net, image, file_name=args_opt.output_file, file_format="AIR") diff --git a/model_zoo/official/cv/warpctc/src/warpctc.py b/model_zoo/official/cv/warpctc/src/warpctc.py index 5ac3c24352f..3027999f833 100755 --- a/model_zoo/official/cv/warpctc/src/warpctc.py +++ b/model_zoo/official/cv/warpctc/src/warpctc.py @@ -32,6 +32,7 @@ class StackedRNN(nn.Cell): batch_size(int): batch size of input data, default is 64 hidden_size(int): the hidden size in LSTM layers, default is 512 """ + def __init__(self, input_size, batch_size=64, hidden_size=512): super(StackedRNN, self).__init__() self.batch_size = batch_size @@ -40,22 +41,25 @@ class StackedRNN(nn.Cell): self.reshape = P.Reshape() self.cast = P.Cast() k = (1 / hidden_size) ** 0.5 - self.h1 = Tensor(np.zeros(shape=(batch_size, hidden_size)).astype(np.float16)) - self.c1 = Tensor(np.zeros(shape=(batch_size, hidden_size)).astype(np.float16)) - self.w1 = Parameter(np.random.uniform(-k, k, (4 * hidden_size, input_size + hidden_size, 1, 1)) - .astype(np.float16), name="w1") - self.w2 = Parameter(np.random.uniform(-k, k, (4 * hidden_size, hidden_size + hidden_size, 1, 1)) - .astype(np.float16), name="w2") - self.b1 = Parameter(np.random.uniform(-k, k, (4 * hidden_size, 1, 1, 1)).astype(np.float16), name="b1") - self.b2 = Parameter(np.random.uniform(-k, k, (4 * hidden_size, 1, 1, 1)).astype(np.float16), name="b2") - self.h2 = Tensor(np.zeros(shape=(batch_size, hidden_size)).astype(np.float16)) - self.c2 = Tensor(np.zeros(shape=(batch_size, hidden_size)).astype(np.float16)) + self.rnn1 = P.DynamicRNN(forget_bias=0.0) + self.rnn2 = P.DynamicRNN(forget_bias=0.0) - self.basic_lstm_cell = P.BasicLSTMCell(keep_prob=1.0, forget_bias=0.0, state_is_tuple=True, activation="tanh") + self.w1 = Parameter(np.random.uniform(-k, k, (input_size + hidden_size, 4 * hidden_size)).astype(np.float16), + name="w1") + self.w2 = Parameter(np.random.uniform(-k, k, (hidden_size + hidden_size, 4 * hidden_size)).astype(np.float16), + name="w2") + self.b1 = Parameter(np.random.uniform(-k, k, (4 * hidden_size)).astype(np.float16), name="b1") + self.b2 = Parameter(np.random.uniform(-k, k, (4 * hidden_size)).astype(np.float16), name="b2") + + self.h1 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16)) + self.h2 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16)) + + self.c1 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16)) + self.c2 = Tensor(np.zeros(shape=(1, batch_size, hidden_size)).astype(np.float16)) self.fc_weight = np.random.random((self.num_classes, hidden_size)).astype(np.float32) - self.fc_bias = np.random.random((self.num_classes)).astype(np.float32) + self.fc_bias = np.random.random(self.num_classes).astype(np.float32) self.fc = nn.Dense(in_channels=hidden_size, out_channels=self.num_classes, weight_init=Tensor(self.fc_weight), bias_init=Tensor(self.fc_bias)) @@ -64,29 +68,22 @@ class StackedRNN(nn.Cell): self.expand_dims = P.ExpandDims() self.concat = P.Concat() self.transpose = P.Transpose() + self.squeeze = P.Squeeze(axis=0) def construct(self, x): x = self.cast(x, mstype.float16) x = self.transpose(x, (3, 0, 2, 1)) x = self.reshape(x, (-1, self.batch_size, self.input_size)) - h1 = self.h1 - c1 = self.c1 - h2 = self.h2 - c2 = self.c2 - c1, h1, _, _, _, _, _ = self.basic_lstm_cell(x[0, :, :], h1, c1, self.w1, self.b1) - c2, h2, _, _, _, _, _ = self.basic_lstm_cell(h1, h2, c2, self.w2, self.b2) - - h2_after_fc = self.fc(h2) - output = self.expand_dims(h2_after_fc, 0) - for i in range(1, F.shape(x)[0]): - c1, h1, _, _, _, _, _ = self.basic_lstm_cell(x[i, :, :], h1, c1, self.w1, self.b1) - c2, h2, _, _, _, _, _ = self.basic_lstm_cell(h1, h2, c2, self.w2, self.b2) - - h2_after_fc = self.fc(h2) - h2_after_fc = self.expand_dims(h2_after_fc, 0) - output = self.concat((output, h2_after_fc)) + y1, _, _, _, _, _, _, _ = self.rnn1(x, self.w1, self.b1, None, self.h1, self.c1) + y2, _, _, _, _, _, _, _ = self.rnn2(y1, self.w2, self.b2, None, self.h2, self.c2) + output = () + for i in range(F.shape(x)[0]): + y2_after_fc = self.fc(self.squeeze(y2[i:i + 1:1])) + y2_after_fc = self.expand_dims(y2_after_fc, 0) + output += (y2_after_fc,) + output = self.concat(output) return output @@ -101,6 +98,7 @@ class StackedRNNForGPU(nn.Cell): hidden_size(int): the hidden size in LSTM layers, default is 512 num_layer(int): the number of layer of LSTM. """ + def __init__(self, input_size, batch_size=64, hidden_size=512, num_layer=2): super(StackedRNNForGPU, self).__init__() self.batch_size = batch_size diff --git a/model_zoo/official/cv/warpctc/train.py b/model_zoo/official/cv/warpctc/train.py index a3d37bbc941..07614851915 100755 --- a/model_zoo/official/cv/warpctc/train.py +++ b/model_zoo/official/cv/warpctc/train.py @@ -42,7 +42,7 @@ parser.add_argument('--platform', type=str, default='Ascend', choices=['Ascend', parser.set_defaults(run_distribute=False) args_opt = parser.parse_args() -context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False) +context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform) if args_opt.platform == 'Ascend': device_id = int(os.getenv('DEVICE_ID')) context.set_context(device_id=device_id)