From c59193644620886becc9a3cfd7518ad74e6a7986 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Tue, 11 Dec 2018 21:49:56 +0100
Subject: [PATCH] updates

---
 requirements.txt | 3 ---
 train.py         | 7 ++++---
 utils/utils.py   | 2 +-
 3 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 2e16f2e3..2d57893b 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,5 @@
 # pip3 install -U -r requirements.txt
 numpy
-scipy
 opencv-python
 torch
 matplotlib
-tqdm
-h5py
\ No newline at end of file
diff --git a/train.py b/train.py
index ef8466c7..e244f495 100644
--- a/train.py
+++ b/train.py
@@ -107,11 +107,12 @@ def train(
     model_info(model)
     t0, t1 = time.time(), time.time()
     mean_recall, mean_precision = 0, 0
-    print('%11s' * 16 % (
-        'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
     for epoch in range(epochs):
         epoch += start_epoch
 
+        print(('%8s%12s' + '%10s' * 14) % ('Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R',
+                                              'nTargets', 'TP', 'FP', 'FN', 'time'))
+
         # Update scheduler (automatic)
         # scheduler.step()
 
@@ -178,7 +179,7 @@ def train(
                 if k.sum() > 0:
                     mean_recall = recall[k].mean()
 
-            s = ('%11s%11s' + '%11.3g' * 14) % (
+            s = ('%8s%12s' + '%10.3g' * 14) % (
                 '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
                 rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
                 rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
diff --git a/utils/utils.py b/utils/utils.py
index 12d161bd..293c5961 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -33,7 +33,7 @@ def model_info(model):  # Plots a line-by-line description of a PyTorch model
     print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
     for i, (name, p) in enumerate(model.named_parameters()):
         name = name.replace('module_list.', '')
-        print('%5g %50s %9s %12g %20s %12g %12g' % (
+        print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
             i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
     print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))