modify resnet101 scripts for pylint

example/resnet101_imagenet/README.md

@@ -54,9 +54,6 @@ Parameters for both training and evaluating can be set in config.py.
 "save_checkpoint_steps": 500,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
 "keep_checkpoint_max": 40,        # only keep the last keep_checkpoint_max checkpoint
 "save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
-"lr_init": 0.01,                  # initial learning rate
-"lr_end": 0.00001,                # final learning rate
-"lr_max": 0.1,                    # maximum learning rate
 "warmup_epochs": 0,               # number of warmup epoch
 "lr_decay_mode": "cosine"         # decay mode for generating learning rate
 "label_smooth": 1,                # label_smooth

example/resnet101_imagenet/config.py

@@ -31,9 +31,6 @@ config = ed({
     "save_checkpoint_steps": 500,
     "keep_checkpoint_max": 40,
     "save_checkpoint_path": "./",
-    "lr_init": 0.01,
-    "lr_end": 0.00001,
-    "lr_max": 0.1,
     "warmup_epochs": 0,
     "lr_decay_mode": "cosine",
     "label_smooth": 1,

example/resnet101_imagenet/lr_generator.py

@@ -50,63 +50,3 @@ def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch):
             lr = base_lr * decayed
         lr_each_step.append(lr)
     return np.array(lr_each_step).astype(np.float32)
-
-def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
-    """
-    generate learning rate array
-
-    Args:
-       global_step(int): total steps of the training
-       lr_init(float): init learning rate
-       lr_end(float): end learning rate
-       lr_max(float): max learning rate
-       warmup_epochs(int): number of warmup epochs
-       total_epochs(int): total epoch of training
-       steps_per_epoch(int): steps of one epoch
-       lr_decay_mode(string): learning rate decay mode, including steps, poly or default
-
-    Returns:
-       np.array, learning rate array
-    """
-    lr_each_step = []
-    total_steps = steps_per_epoch * total_epochs
-    warmup_steps = steps_per_epoch * warmup_epochs
-    if lr_decay_mode == 'steps':
-        decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
-        for i in range(total_steps):
-            if i < decay_epoch_index[0]:
-                lr = lr_max
-            elif i < decay_epoch_index[1]:
-                lr = lr_max * 0.1
-            elif i < decay_epoch_index[2]:
-                lr = lr_max * 0.01
-            else:
-                lr = lr_max * 0.001
-            lr_each_step.append(lr)
-    elif lr_decay_mode == 'poly':
-        if warmup_steps != 0:
-            inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
-        else:
-            inc_each_step = 0
-        for i in range(total_steps):
-            if i < warmup_steps:
-                lr = float(lr_init) + inc_each_step * float(i)
-            else:
-                base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
-                lr = float(lr_max) * base * base
-                if lr < 0.0:
-                    lr = 0.0
-            lr_each_step.append(lr)
-    else:
-        for i in range(total_steps):
-            if i < warmup_steps:
-                lr = lr_init + (lr_max - lr_init) * i / warmup_steps
-            else:
-                lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
-            lr_each_step.append(lr)
-
-    current_step = global_step
-    lr_each_step = np.array(lr_each_step).astype(np.float32)
-    learning_rate = lr_each_step[current_step:]
-
-    return learning_rate

example/resnet101_imagenet/train.py

@@ -19,7 +19,7 @@ import argparse
 import random
 import numpy as np
 from dataset import create_dataset
-from lr_generator import get_lr, warmup_cosine_annealing_lr
+from lr_generator import warmup_cosine_annealing_lr
 from config import config
 from mindspore import context
 from mindspore import Tensor
@@ -32,9 +32,9 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager
 import mindspore.dataset.engine as de
 from mindspore.communication.management import init
 import mindspore.nn as nn
+import mindspore.common.initializer as weight_init
 from crossentropy import CrossEntropy
 from var_init import default_recurisive_init, KaimingNormal
-import mindspore.common.initializer as weight_init
 
 random.seed(1)
 np.random.seed(1)
@@ -72,7 +72,7 @@ if __name__ == '__main__':
     net = resnet101(class_num=config.class_num)
     # weight init
     default_recurisive_init(net)
-    for name, cell in net.cells_and_names():
+    for _, cell in net.cells_and_names():
         if isinstance(cell, nn.Conv2d):
             cell.weight.default_input = weight_init.initializer(KaimingNormal(a=math.sqrt(5),
                                                                               mode='fan_out', nonlinearity='relu'),
@@ -83,17 +83,12 @@ if __name__ == '__main__':
     loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
     if args_opt.do_train:
         dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
-                repeat_num=epoch_size, batch_size=config.batch_size)
+                                 repeat_num=epoch_size, batch_size=config.batch_size)
         step_size = dataset.get_dataset_size()
         loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
 
-        # learning rate strategy
+        # learning rate strategy with cosine
-        if config.lr_decay_mode == 'cosine':
+        lr = Tensor(warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size))
-            lr = Tensor(warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size))
-        else:
-            lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
-                               warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
-                               lr_decay_mode='poly'))
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
                        config.weight_decay, config.loss_scale)
         model = Model(net, loss_fn=loss, optimizer=opt, amp_level='O2', keep_batchnorm_fp32=False,

example/resnet101_imagenet/var_init.py

@@ -18,10 +18,10 @@ import numpy as np
 from mindspore.common import initializer as init
 import mindspore.nn as nn
 from mindspore import Tensor
- 
+
 def calculate_gain(nonlinearity, param=None):
     r"""Return the recommended gain value for the given nonlinearity function.
-    The values are as follows: 
+    The values are as follows:
     ================= ====================================================
     nonlinearity      gain
     ================= ====================================================
@@ -37,12 +37,13 @@ def calculate_gain(nonlinearity, param=None):
         param: optional parameter for the non-linear function
     """
     linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
+    gain = 0
     if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
-        return 1
+        gain = 1
     elif nonlinearity == 'tanh':
-        return 5.0 / 3
+        gain = 5.0 / 3
     elif nonlinearity == 'relu':
-        return math.sqrt(2.0)
+        gain = math.sqrt(2.0)
     elif nonlinearity == 'leaky_relu':
         if param is None:
             negative_slope = 0.01
@@ -51,15 +52,16 @@ def calculate_gain(nonlinearity, param=None):
             negative_slope = param
         else:
             raise ValueError("negative_slope {} not a valid number".format(param))
-        return math.sqrt(2.0 / (1 + negative_slope ** 2))
+        gain = math.sqrt(2.0 / (1 + negative_slope ** 2))
     else:
         raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
-    
+    return gain
+
 def _calculate_correct_fan(array, mode):
     mode = mode.lower()
     valid_modes = ['fan_in', 'fan_out']
     if mode not in valid_modes:
-        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes)) 
+        raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
     fan_in, fan_out = _calculate_fan_in_and_fan_out(array)
     return fan_in if mode == 'fan_in' else fan_out
 
@@ -83,13 +85,12 @@ def kaiming_uniform_(array, a=0, mode='fan_in', nonlinearity='leaky_relu'):
             backwards pass.
         nonlinearity: the non-linear function (`nn.functional` name),
             recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
-    """ 
+    """
     fan = _calculate_correct_fan(array, mode)
     gain = calculate_gain(nonlinearity, a)
     std = gain / math.sqrt(fan)
     bound = math.sqrt(3.0) * std  # Calculate uniform bounds from standard deviation
     return np.random.uniform(-bound, bound, array.shape)
- 
 
 def kaiming_normal_(array, a=0, mode='fan_in', nonlinearity='leaky_relu'):
     r"""Fills the input `Tensor` with values according to the method
@@ -97,12 +98,10 @@ def kaiming_normal_(array, a=0, mode='fan_in', nonlinearity='leaky_relu'):
     performance on ImageNet classification` - He, K. et al. (2015), using a
     normal distribution. The resulting tensor will have values sampled from
     :math:`\mathcal{N}(0, \text{std}^2)` where
- 
     .. math::
         \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
-
     Also known as He initialization.
- 
+
     Args:
         array: an n-dimensional `tensor`
         a: the negative slope of the rectifier used after this layer (only
@@ -118,13 +117,12 @@ def kaiming_normal_(array, a=0, mode='fan_in', nonlinearity='leaky_relu'):
     gain = calculate_gain(nonlinearity, a)
     std = gain / math.sqrt(fan)
     return np.random.normal(0, std, array.shape)
- 
+
 def _calculate_fan_in_and_fan_out(array):
     """calculate the fan_in and fan_out for input array"""
     dimensions = len(array.shape)
     if dimensions < 2:
         raise ValueError("Fan in and fan out can not be computed for array with fewer than 2 dimensions")
- 
     num_input_fmaps = array.shape[1]
     num_output_fmaps = array.shape[0]
     receptive_field_size = 1
@@ -132,19 +130,30 @@ def _calculate_fan_in_and_fan_out(array):
         receptive_field_size = array[0][0].size
     fan_in = num_input_fmaps * receptive_field_size
     fan_out = num_output_fmaps * receptive_field_size
- 
     return fan_in, fan_out
- 
+
+def assignment(arr, num):
+    """Assign the value of num to arr"""
+    if arr.shape == ():
+        arr = arr.reshape((1))
+        arr[:] = num
+        arr = arr.reshape(())
+    else:
+        if isinstance(num, np.ndarray):
+            arr[:] = num[:]
+        else:
+            arr[:] = num
+    return arr
+
 class KaimingUniform(init.Initializer):
     def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
         super(KaimingUniform, self).__init__()
         self.a = a
         self.mode = mode
         self.nonlinearity = nonlinearity
- 
     def _initialize(self, arr):
         tmp = kaiming_uniform_(arr, self.a, self.mode, self.nonlinearity)
-        init._assignment(arr, tmp) 
+        assignment(arr, tmp)
 
 class KaimingNormal(init.Initializer):
     def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
@@ -152,33 +161,32 @@ class KaimingNormal(init.Initializer):
         self.a = a
         self.mode = mode
         self.nonlinearity = nonlinearity
- 
     def _initialize(self, arr):
         tmp = kaiming_normal_(arr, self.a, self.mode, self.nonlinearity)
-        init._assignment(arr, tmp)
+        assignment(arr, tmp)
 
 def default_recurisive_init(custom_cell):
     """weight init for conv2d and dense"""
-    for name, cell in custom_cell.cells_and_names():
+    for _, cell in custom_cell.cells_and_names():
         if isinstance(cell, nn.Conv2d):
-            cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)), 
+            cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
-                    cell.weight.default_input.shape(), 
+                                                         cell.weight.default_input.shape(),
-                    cell.weight.default_input.dtype())
+                                                         cell.weight.default_input.dtype())
             if cell.bias is not None:
                 fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.default_input.asnumpy())
                 bound = 1 / math.sqrt(fan_in)
-                cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, 
+                cell.bias.default_input = Tensor(np.random.uniform(-bound, bound,
-                    cell.bias.default_input.shape()), 
+                                                                   cell.bias.default_input.shape()),
-                    cell.bias.default_input.dtype())
+                                                 cell.bias.default_input.dtype())
         elif isinstance(cell, nn.Dense):
-            cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)), 
+            cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
-                    cell.weight.default_input.shape(), 
+                                                         cell.weight.default_input.shape(),
-                    cell.weight.default_input.dtype())
+                                                         cell.weight.default_input.dtype())
             if cell.bias is not None:
                 fan_in, _ = _calculate_fan_in_and_fan_out(cell.weight.default_input.asnumpy())
                 bound = 1 / math.sqrt(fan_in)
-                cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, 
+                cell.bias.default_input = Tensor(np.random.uniform(-bound, bound,
-                    cell.bias.default_input.shape()), 
+                                                                   cell.bias.default_input.shape()),
-                    cell.bias.default_input.dtype())
+                                                 cell.bias.default_input.dtype())
         elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
             pass

mindspore/model_zoo/resnet.py

@@ -279,4 +279,4 @@ def resnet101(class_num=1001):
                   [64, 256, 512, 1024],
                   [256, 512, 1024, 2048],
                   [1, 2, 2, 2],
-                  class_num)
+                  class_num)