utils.py in references can't work with pytorch-cpu (#1023)

* can't work with pytorch-cpu fixed

utils.py can't work with pytorch-cpu because of this line of code `memory=torch.cuda.max_memory_allocated()`

* can't work with pytorch-cpu fixed

utils.py can't work with pytorch-cpu because of this line of code 'memory=torch.cuda.max_memory_allocated()'

references/classification/utils.py

@@ -115,6 +115,7 @@ class MetricLogger(object):
         iter_time = SmoothedValue(fmt='{avg:.4f}')
         data_time = SmoothedValue(fmt='{avg:.4f}')
         space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        if torch.cuda.is_available():
             log_msg = self.delimiter.join([
                 header,
                 '[{0' + space_fmt + '}/{1}]',
@@ -124,6 +125,15 @@ class MetricLogger(object):
                 'data: {data}',
                 'max mem: {memory:.0f}'
             ])
+        else:
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'data: {data}'
+            ])
         MB = 1024.0 * 1024.0
         for obj in iterable:
             data_time.update(time.time() - end)
@@ -132,11 +142,17 @@ class MetricLogger(object):
             if i % print_freq == 0:
                 eta_seconds = iter_time.global_avg * (len(iterable) - i)
                 eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
                     print(log_msg.format(
                         i, len(iterable), eta=eta_string,
                         meters=str(self),
                         time=str(iter_time), data=str(data_time),
                         memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
             i += 1
             end = time.time()
         total_time = time.time() - start_time

references/detection/utils.py

@@ -188,6 +188,7 @@ class MetricLogger(object):
         iter_time = SmoothedValue(fmt='{avg:.4f}')
         data_time = SmoothedValue(fmt='{avg:.4f}')
         space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        if torch.cuda.is_available():
             log_msg = self.delimiter.join([
                 header,
                 '[{0' + space_fmt + '}/{1}]',
@@ -197,6 +198,15 @@ class MetricLogger(object):
                 'data: {data}',
                 'max mem: {memory:.0f}'
             ])
+        else:
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'data: {data}'
+            ])
         MB = 1024.0 * 1024.0
         for obj in iterable:
             data_time.update(time.time() - end)
@@ -205,11 +215,17 @@ class MetricLogger(object):
             if i % print_freq == 0 or i == len(iterable) - 1:
                 eta_seconds = iter_time.global_avg * (len(iterable) - i)
                 eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
                     print(log_msg.format(
                         i, len(iterable), eta=eta_string,
                         meters=str(self),
                         time=str(iter_time), data=str(data_time),
                         memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
             i += 1
             end = time.time()
         total_time = time.time() - start_time

references/segmentation/utils.py

@@ -161,6 +161,7 @@ class MetricLogger(object):
         iter_time = SmoothedValue(fmt='{avg:.4f}')
         data_time = SmoothedValue(fmt='{avg:.4f}')
         space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        if torch.cuda.is_available():
             log_msg = self.delimiter.join([
                 header,
                 '[{0' + space_fmt + '}/{1}]',
@@ -170,6 +171,15 @@ class MetricLogger(object):
                 'data: {data}',
                 'max mem: {memory:.0f}'
             ])
+        else:
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'data: {data}'
+            ])
         MB = 1024.0 * 1024.0
         for obj in iterable:
             data_time.update(time.time() - end)
@@ -178,11 +188,17 @@ class MetricLogger(object):
             if i % print_freq == 0:
                 eta_seconds = iter_time.global_avg * (len(iterable) - i)
                 eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
                     print(log_msg.format(
                         i, len(iterable), eta=eta_string,
                         meters=str(self),
                         time=str(iter_time), data=str(data_time),
                         memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
             i += 1
             end = time.time()
         total_time = time.time() - start_time