diff --git a/example/yolov3_coco2017/dataset.py b/example/yolov3_coco2017/dataset.py index 9c6a0f362d2..23d34e0f4fa 100644 --- a/example/yolov3_coco2017/dataset.py +++ b/example/yolov3_coco2017/dataset.py @@ -18,8 +18,8 @@ from __future__ import division import os import numpy as np -from PIL import Image from matplotlib.colors import rgb_to_hsv, hsv_to_rgb +from PIL import Image import mindspore.dataset as de from mindspore.mindrecord import FileWriter import mindspore.dataset.transforms.vision.c_transforms as C diff --git a/mindspore/_akg/__init__.py b/mindspore/_akg/__init__.py index e3dceaf35e7..a343e3532ab 100644 --- a/mindspore/_akg/__init__.py +++ b/mindspore/_akg/__init__.py @@ -16,6 +16,9 @@ from __future__ import absolute_import as _abs import sys import os +from .op_build import op_build +from .message import compilewithjson + def AKGAddPath(): """_akg add path.""" @@ -58,6 +61,3 @@ class AKGMetaPathLoader: sys.meta_path.insert(0, AKGMetaPathFinder()) - -from .op_build import op_build -from .message import compilewithjson diff --git a/mindspore/nn/optim/ftrl.py b/mindspore/nn/optim/ftrl.py index 2bc329f42d8..e6f658acae1 100644 --- a/mindspore/nn/optim/ftrl.py +++ b/mindspore/nn/optim/ftrl.py @@ -14,7 +14,6 @@ # ============================================================================ """FTRL""" from mindspore.ops import functional as F, composite as C, operations as P -from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore.common import Tensor import mindspore.common.dtype as mstype @@ -23,6 +22,8 @@ from mindspore._checkparam import Rel from .optimizer import Optimizer, apply_decay, grad_scale ftrl_opt = C.MultitypeFuncGraph("ftrl_opt") + + @ftrl_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor") def _tensor_run_opt(opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment): """Apply ftrl optimizer to the weight parameter.""" @@ -30,8 +31,10 @@ def _tensor_run_opt(opt, learning_rate, l1, l2, lr_power, linear, gradient, weig success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power)) return success + def _check_param(initial_accum, learning_rate, lr_power, l1, l2, use_locking, loss_scale=1.0, weight_decay=0.0, prim_name=None): + """Check param.""" validator.check_value_type("initial_accum", initial_accum, [float], prim_name) validator.check_number("initial_accum", initial_accum, 0.0, Rel.GE, prim_name) @@ -104,7 +107,7 @@ class FTRL(Optimizer): self.lr_power = lr_power self.reciprocal_scale = 1.0 / loss_scale self.weight_decay = weight_decay - self.decay_tf = tuple((lambda:True)() for x in self.parameters) + self.decay_tf = tuple((lambda: True)() for x in self.parameters) self.hyper_map = C.HyperMap() self.opt = P.ApplyFtrl(use_locking=use_locking) self.one = Tensor(1, mstype.int32) @@ -118,5 +121,6 @@ class FTRL(Optimizer): if self.reciprocal_scale != 1.0: grads = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), grads) lr = self.learning_rate - success = self.hyper_map(F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power), linear, grads, params, moments) + success = self.hyper_map(F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power), + linear, grads, params, moments) return success diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index e574dd25663..ecb707ed51a 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -2063,7 +2063,7 @@ class LSTM(PrimitiveWithInfer): return (y_shape, h_shape, c_shape, reserved_shape, state_shape) def infer_dtype(self, x_dtype, h_dtype, c_dtype, w_dtype): - args = {'x': x_dtype, 'h': h_dtype, 'c': c_dtype, 'w': w_dtype} + args = {'x': x_dtype, 'h': h_dtype, 'c': c_dtype, 'w': w_dtype} validator.check_tensor_type_same(args, (mstype.float32, mstype.float16), self.name) return (x_dtype, x_dtype, x_dtype, x_dtype, x_dtype) @@ -2691,8 +2691,8 @@ class ConfusionMulGrad(PrimitiveWithInfer): """ @prim_attr_register - def __init__(self, axis = (), keep_dims = False): - self.init_prim_io_names(inputs = ["input0", "input1", "input2"], outputs = ["output0", "output1"]) + def __init__(self, axis=(), keep_dims=False): + self.init_prim_io_names(inputs=["input0", "input1", "input2"], outputs=["output0", "output1"]) self.axis_ = validator.check_value_type("axis", axis, [int, tuple, list], self.name) self.keep_dims_ = validator.check_value_type("keep_dims", keep_dims, [bool], self.name) diff --git a/mindspore/train/amp.py b/mindspore/train/amp.py index 917b4c3359d..2e758b0e9dd 100644 --- a/mindspore/train/amp.py +++ b/mindspore/train/amp.py @@ -41,6 +41,7 @@ class OutputTo16(nn.Cell): def _do_keep_batchnorm_fp32(network): + """Do keep batchnorm fp32.""" cells = network.name_cells() change = False for name in cells: @@ -68,6 +69,7 @@ _config_level = { def _check_kwargs(key_words): + """Check kwargs.""" for arg in key_words: if arg not in ['cast_model_type', 'keep_batchnorm_fp32', 'loss_scale_manager']: raise ValueError(f"Unsupported arg '{arg}'") @@ -84,6 +86,7 @@ def _check_kwargs(key_words): def _add_loss_network(network, loss_fn, cast_model_type): + """Add loss network.""" class WithLossCell(nn.Cell): "Wrap loss for amp. Cast network output back to float32" diff --git a/mindspore/train/callback.py b/mindspore/train/callback.py index b9635acc62e..c8ce5d22ef4 100644 --- a/mindspore/train/callback.py +++ b/mindspore/train/callback.py @@ -683,13 +683,14 @@ class LossMonitor(Callback): class TimeMonitor(Callback): + """Time Monitor.""" def __init__(self, data_size): super(TimeMonitor, self).__init__() self.data_size = data_size def epoch_begin(self, run_context): self.epoch_time = time.time() - + def epoch_end(self, run_context): epoch_mseconds = (time.time() - self.epoch_time) * 1000 per_step_mseconds = epoch_mseconds / self.data_size @@ -701,4 +702,3 @@ class TimeMonitor(Callback): def step_end(self, run_context): step_mseconds = (time.time() - self.step_time) * 1000 print('step time', step_mseconds, flush=True) - diff --git a/mindspore/train/model.py b/mindspore/train/model.py index 36e94170955..66b03ce06c7 100755 --- a/mindspore/train/model.py +++ b/mindspore/train/model.py @@ -122,7 +122,7 @@ class Model: def _check_kwargs(self, kwargs): for arg in kwargs: if arg not in ['loss_scale_manager', 'keep_batchnorm_fp32']: - raise ValueError(f"Unsupport arg '{arg}'") + raise ValueError(f"Unsupport arg '{arg}'") def _build_train_network(self): """Build train network""" @@ -130,17 +130,17 @@ class Model: if self._optimizer: if self._loss_scale_manager_set: network = amp.build_train_network(network, - self._optimizer, - self._loss_fn, - level=self._amp_level, - loss_scale_manager=self._loss_scale_manager, - keep_batchnorm_fp32=self._keep_bn_fp32) + self._optimizer, + self._loss_fn, + level=self._amp_level, + loss_scale_manager=self._loss_scale_manager, + keep_batchnorm_fp32=self._keep_bn_fp32) else: network = amp.build_train_network(network, - self._optimizer, - self._loss_fn, - level=self._amp_level, - keep_batchnorm_fp32=self._keep_bn_fp32) + self._optimizer, + self._loss_fn, + level=self._amp_level, + keep_batchnorm_fp32=self._keep_bn_fp32) elif self._loss_fn: network = nn.WithLossCell(network, self._loss_fn) # If need to check if loss_fn is not None, but optimizer is None @@ -273,14 +273,14 @@ class Model: # remove later to deal with loop sink need_wrap = False if not hasattr(train_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink") \ - and not context.get_context("enable_ge"): + and not context.get_context("enable_ge"): need_wrap = True dataset_helper = DatasetHelper(train_dataset) # remove later to deal with loop sink if need_wrap: self._train_network = nn.DataWrapper(self._train_network, *(dataset_helper.types_shapes()), - train_dataset.__ME_INITED__) + train_dataset.__ME_INITED__) cb_params.train_network = self._train_network self._train_network.set_train() @@ -440,7 +440,7 @@ class Model: # remove later to deal with loop sink need_wrap = False if not hasattr(valid_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink") \ - and not context.get_context("enable_ge"): + and not context.get_context("enable_ge"): need_wrap = True valid_dataset.__loop_size__ = 1 @@ -449,7 +449,7 @@ class Model: # remove later to deal with loop sink if need_wrap: self._eval_network = nn.DataWrapper(self._eval_network, *(dataset_helper.types_shapes()), - valid_dataset.__ME_INITED__) + valid_dataset.__ME_INITED__) self._eval_network.set_train(mode=False) self._eval_network.phase = 'eval' diff --git a/tests/mindspore_test_framework/apps/test_bert_parts.py b/tests/mindspore_test_framework/apps/test_bert_parts.py index 226d175c3d7..944ea078420 100644 --- a/tests/mindspore_test_framework/apps/test_bert_parts.py +++ b/tests/mindspore_test_framework/apps/test_bert_parts.py @@ -174,8 +174,7 @@ test_sets = [ embedding_shape=[1, 128, 768], use_one_hot_embeddings=True, initializer_range=0.02), 1, 1), { - 'init_param_with': lambda shp: np.ones(shp).astype(np.float32) - }), + 'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}), 'desc_inputs': [input_ids], 'desc_bprop': [[128]]}), ('EmbeddingLookup_multi_outputs_init_param', { @@ -184,8 +183,7 @@ test_sets = [ embedding_shape=[1, 128, 768], use_one_hot_embeddings=False, initializer_range=0.02), { - 'init_param_with': lambda shp: np.ones(shp).astype(np.float32) - }), + 'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}), 'desc_inputs': [input_ids], 'desc_bprop': [[1, 128, 768], [128]]}), ('EmbeddingLookup_multi_outputs_grad_with_no_sens', { @@ -194,8 +192,7 @@ test_sets = [ embedding_shape=[1, 128, 768], use_one_hot_embeddings=False, initializer_range=0.02), { - 'init_param_with': lambda shp: np.ones(shp).astype(np.float32) - }), + 'init_param_with': lambda shp: np.ones(shp).astype(np.float32)}), 'desc_inputs': [input_ids]}), ('GetMaskedLMOutput_grad_with_no_sens', { 'block': GetMaskedLMOutput(BertConfig(batch_size=1)), diff --git a/tests/mindspore_test_framework/components/executor/check_exceptions.py b/tests/mindspore_test_framework/components/executor/check_exceptions.py index fe57a3d287e..a4eb1cd8a04 100644 --- a/tests/mindspore_test_framework/components/executor/check_exceptions.py +++ b/tests/mindspore_test_framework/components/executor/check_exceptions.py @@ -44,4 +44,4 @@ class CheckExceptionsEC(IExectorComponent): raise Exception(f"Expect {e}, but got {sys.exc_info()[0]}") if error_kws and any(keyword not in str(exec_info.value) for keyword in error_kws): raise ValueError('Error message `{}` does not contain all keywords `{}`'.format( - str(exec_info.value), error_kws)) + str(exec_info.value), error_kws))