tight_layout=True

utils/utils.py

@@ -5,6 +5,7 @@ import random
 import shutil
 import subprocess
 import time
+from copy import copy
 from pathlib import Path
 from sys import platform
 
@@ -370,8 +371,8 @@ def compute_loss(p, targets, model):  # predictions, targets, model
             ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
 
             # GIoU
-            pxy = torch.sigmoid(ps[:, 0:2])
+            pxy = ps[:, :2].sigmoid()
-            pwh = torch.exp(ps[:, 2:4]).clamp(max=1E3) * anchors[i]
+            pwh = ps[:, 2:4].exp().clamp(max=1E3) * anchors[i]
             pbox = torch.cat((pxy, pwh), 1)  # predicted box
             giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou(prediction, target)
             lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean()  # giou loss
@@ -416,7 +417,6 @@ def build_targets(p, targets, model):
     style = None
     multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
     for i, j in enumerate(model.yolo_layers):
-        # get number of grid points and anchor vec for this yolo layer
         anchors = model.module.module_list[j].anchor_vec if multi_gpu else model.module_list[j].anchor_vec
         gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
         na = anchors.shape[0]  # number of anchors
@@ -573,7 +573,7 @@ def strip_optimizer(f='weights/best.pt'):  # from utils.utils import *; strip_op
     torch.save(x, f)
 
 
-def create_backbone(f='weights/last.pt'):  # from utils.utils import *; create_backbone()
+def create_backbone(f='weights/best.pt'):  # from utils.utils import *; create_backbone()
     # create a backbone from a *.pt file
     x = torch.load(f, map_location=torch.device('cpu'))
     x['optimizer'] = None
@@ -816,12 +816,12 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
     tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
     color = color or [random.randint(0, 255) for _ in range(3)]
     c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
-    cv2.rectangle(img, c1, c2, color, thickness=tl)
+    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
     if label:
         tf = max(tl - 1, 1)  # font thickness
         t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
         c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(img, c1, c2, color, -1)  # filled
+        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
         cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
 
 
@@ -928,22 +928,34 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
     return mosaic
 
 
+def plot_lr_scheduler(optimizer, scheduler, epochs=300):
+    # Plot LR simulating training for full epochs
+    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
+    y = []
+    for _ in range(epochs):
+        scheduler.step()
+        y.append(optimizer.param_groups[0]['lr'])
+    plt.plot(y, '.-', label='LR')
+    plt.xlabel('epoch')
+    plt.ylabel('LR')
+    plt.tight_layout()
+    plt.savefig('LR.png', dpi=200)
+
+
 def plot_test_txt():  # from utils.utils import *; plot_test()
     # Plot test.txt histograms
     x = np.loadtxt('test.txt', dtype=np.float32)
     box = xyxy2xywh(x[:, :4])
     cx, cy = box[:, 0], box[:, 1]
 
-    fig, ax = plt.subplots(1, 1, figsize=(6, 6))
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
     ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
     ax.set_aspect('equal')
-    fig.tight_layout()
     plt.savefig('hist2d.png', dpi=300)
 
-    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
     ax[0].hist(cx, bins=600)
     ax[1].hist(cy, bins=600)
-    fig.tight_layout()
     plt.savefig('hist1d.png', dpi=200)
 
 
@@ -951,22 +963,45 @@ def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
     # Plot targets.txt histograms
     x = np.loadtxt('targets.txt', dtype=np.float32).T
     s = ['x targets', 'y targets', 'width targets', 'height targets']
-    fig, ax = plt.subplots(2, 2, figsize=(8, 8))
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
     ax = ax.ravel()
     for i in range(4):
         ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
         ax[i].legend()
         ax[i].set_title(s[i])
-    fig.tight_layout()
     plt.savefig('targets.jpg', dpi=200)
 
 
+def plot_labels(labels):
+    # plot dataset labels
+    c, b = labels[:, 0], labels[:, 1:].transpose()  # classees, boxes
+
+    def hist2d(x, y, n=100):
+        xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+        hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+        xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+        yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+        return hist[xidx, yidx]
+
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    ax[0].hist(c, bins=int(c.max() + 1))
+    ax[0].set_xlabel('classes')
+    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
+    ax[1].set_xlabel('x')
+    ax[1].set_ylabel('y')
+    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
+    ax[2].set_xlabel('width')
+    ax[2].set_ylabel('height')
+    plt.savefig('labels.png', dpi=200)
+
+
 def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
     # Plot hyperparameter evolution results in evolve.txt
     x = np.loadtxt('evolve.txt', ndmin=2)
     f = fitness(x)
     # weights = (f - f.min()) ** 2  # for weighted results
-    fig = plt.figure(figsize=(12, 10))
+    fig = plt.figure(figsize=(12, 10), tight_layout=True)
     matplotlib.rc('font', **{'size': 8})
     for i, (k, v) in enumerate(hyp.items()):
         y = x[:, i + 7]
@@ -977,7 +1012,6 @@ def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_re
         plt.plot(y, f, '.')
         plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
         print('%15s: %.3g' % (k, mu))
-    fig.tight_layout()
     plt.savefig('evolve.png', dpi=200)
 
 
@@ -989,7 +1023,7 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
         results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
         n = results.shape[1]  # number of rows
         x = range(start, min(stop, n) if stop else n)
-        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
+        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
         ax = ax.ravel()
         for i in range(5):
             for j in [i, i + 5]:
@@ -1000,13 +1034,12 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
             ax[i].set_title(t[i])
             ax[i].legend()
             ax[i].set_ylabel(f) if i == 0 else None  # add filename
-        fig.tight_layout()
         fig.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
 def plot_results(start=0, stop=0, bucket='', id=()):  # from utils.utils import *; plot_results()
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov3#training
-    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
+    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
     ax = ax.ravel()
     s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
          'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'F1']
@@ -1032,6 +1065,5 @@ def plot_results(start=0, stop=0, bucket='', id=()):  # from utils.utils import
         except:
             print('Warning: Plotting error for %s, skipping file' % f)
 
-    fig.tight_layout()
     ax[1].legend()
     fig.savefig('results.png', dpi=200)