remove torchnet from requirements (#63)

Author: qubvel

requirements.txt

@@ -1,3 +1,2 @@
-torchvision>=0.2.2,<=0.3.0
+torchvision>=0.2.2,<=0.4.0
 pretrainedmodels==0.7.4
-torchnet==0.0.4

segmentation_models_pytorch/utils/meter.py

@@ -0,0 +1,61 @@
+import numpy as np
+
+
+class Meter(object):
+    '''Meters provide a way to keep track of important statistics in an online manner.
+    This class is abstract, but provides a standard interface for all meters to follow.
+    '''
+
+    def reset(self):
+        '''Resets the meter to default settings.'''
+        pass
+
+    def add(self, value):
+        '''Log a new value to the meter
+        Args:
+            value: Next restult to include.
+        '''
+        pass
+
+    def value(self):
+        '''Get the value of the meter in the current state.'''
+        pass
+
+
+class AverageValueMeter(Meter):
+    def __init__(self):
+        super(AverageValueMeter, self).__init__()
+        self.reset()
+        self.val = 0
+
+    def add(self, value, n=1):
+        self.val = value
+        self.sum += value
+        self.var += value * value
+        self.n += n
+
+        if self.n == 0:
+            self.mean, self.std = np.nan, np.nan
+        elif self.n == 1:
+            self.mean = 0.0 + self.sum  # This is to force a copy in torch/numpy
+            self.std = np.inf
+            self.mean_old = self.mean
+            self.m_s = 0.0
+        else:
+            self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n)
+            self.m_s += (value - self.mean_old) * (value - self.mean)
+            self.mean_old = self.mean
+            self.std = np.sqrt(self.m_s / (self.n - 1.0))
+
+    def value(self):
+        return self.mean, self.std
+
+    def reset(self):
+        self.n = 0
+        self.sum = 0.0
+        self.var = 0.0
+        self.val = 0.0
+        self.mean = np.nan
+        self.mean_old = 0.0
+        self.m_s = 0.0
+        self.std = np.nan

segmentation_models_pytorch/utils/train.py

@@ -1,7 +1,7 @@
 import sys
 import torch
 from tqdm import tqdm as tqdm
-from torchnet.meter import AverageValueMeter
+from .meter import AverageValueMeter
 
 
 class Epoch: