[jacinto-ai/pytorch-jacinto-ai-devkit.git] / modules / pytorch_jacinto_ai / xnn / utils / hist_utils.py
diff --git a/modules/pytorch_jacinto_ai/xnn/utils/hist_utils.py b/modules/pytorch_jacinto_ai/xnn/utils/hist_utils.py
--- /dev/null
@@ -0,0 +1,121 @@
+import sys
+import numpy as np
+import cv2
+import torch
+import os
+import matplotlib.pyplot as plt
+
+#from .. import layers as xtensor_layers
+
+#study histogram for 3D tensor. The 1st dim belongs to ch
+#study 2D histogram of each channel
+def comp_hist_tensor3d(x=[], name='tensor', en=True, dir = 'dir_name', log = False, ch_dim=2):
+ if en == False:
+ return
+ root = os.getcwd()
+ path = root + '/checkpoints/debug/'+ dir
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ if ch_dim == 2:
+ for ch in range(x.shape[ch_dim]):
+ x_ch = x[:,:,ch]
+ print('min={}, max={}, std={}, mean={}'.format(x_ch.min(), x_ch.max(), x_ch.std(), x_ch.mean()))
+ plt.hist(x_ch.flatten(), bins=256, log=log) # arguments are passed to np.histogram
+ #plt.title("Histogram with 'auto' bins")
+ #plt.show()
+ plt.savefig('{}/{}_{:03d}.jpg'.format(path, name, ch))
+ plt.close()
+ elif ch_dim == 0:
+ for ch in range(x.shape[ch_dim]):
+ x_ch = x[ch,:,:]
+ print('min={}, max={}, std={}, mean={}'.format(x_ch.min(), x_ch.max(), x_ch.std(), x_ch.mean()))
+ plt.hist(x_ch.flatten(), bins=256, log=log) # arguments are passed to np.histogram
+ #plt.title("Histogram with 'auto' bins")
+ #plt.show()
+ plt.savefig('{}/{}_{:03d}.jpg'.format(path, name, ch))
+ plt.close()
+
+def hist_tensor2D(x_ch=[], dir = 'tensor_dir', name='tensor', en=True, log=False, ch=0):
+ if en == False:
+ return
+ root = os.getcwd()
+ path = root + '/checkpoints/debug/'+ dir
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ print('min={:.3f}, max={:.3f}, std={:.3f}, mean={:.3f}'.format(x_ch.min(), x_ch.max(), x_ch.std(), x_ch.mean()))
+ hist_ary = plt.hist(x_ch.flatten(), bins=256, log=log) # arguments are passed to np.histogram
+
+
+ #plt.title("Histogram with 'auto' bins")
+ #plt.show()
+ plt.savefig('{}/{}_{:03d}.jpg'.format(path, name,ch))
+ plt.close()
+ return hist_ary
+
+def analyze_model(model):
+ num_dead_ch = 0
+ for n, m in model.named_modules():
+ if isinstance(m, torch.nn.Conv2d):
+ if m.weight.shape[1] == 1:
+ for ch in range(m.weight.shape[0]):
+ cur_ch_wt = m.weight[ch][0][...]
+ mn = cur_ch_wt.min()
+ mx = cur_ch_wt.max()
+ mn = mn.cpu().detach().numpy()
+ mx = mx.cpu().detach().numpy()
+ print(n, 'dws weight ch mn mx', ch, mn, mx)
+ #print(n, 'dws weight ch', ch, cur_ch_wt)
+ if max(abs(mn), abs(mx)) <= 1E-40:
+ num_dead_ch += 1
+ else:
+ print(n, 'weight', 'shape', m.weight.shape, m.weight.min(), m.weight.max())
+ if m.bias is not None:
+ print(n, 'bias', m.bias.min(), m.bias.max())
+
+ print("num_dead_ch: ", num_dead_ch)
+
+def study_wts(self, modules):
+ for key, value in modules.items():
+ print(key, value)
+ for key2, value2 in value._modules.items():
+ print(key2, value2)
+ print(value2.weight.shape)
+
+
+def comp_hist(self, x=[], ch_idx=0, name='tensor'):
+ #hist_pred = torch.histc(x.cpu(), bins=256)
+ root = os.getcwd()
+ path = root + '/checkpoints/debug/'+ name
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+ #print(hist_pred)
+ for ch in range(x.shape[1]):
+ x_ch = x[0][ch]
+ plt.hist(x_ch.view(-1).cpu().numpy(), bins=256) # arguments are passed to np.histogram
+ print('min={}, max={}, std={}, mean={}'.format(x_ch.min(), x_ch.max(), x_ch.std(), x_ch.mean()))
+ #plt.title("Histogram with 'auto' bins")
+ #plt.show()
+ plt.savefig('{}/{}_{:03d}.jpg'.format(path, name, ch))
+ plt.close()
+
+
+def store_layer_op(en=False, tensor= [], name='tensor_name'):
+ if en == False:
+ return
+
+ # write tensor
+ tensor = tensor.astype(np.int16)
+ print("writing tensor {} : {} : {} : {} : {}".format(name, tensor.shape, tensor.dtype, tensor.min(), tensor.max()))
+
+ root = os.getcwd()
+ tensor_dir = root + '/checkpoints/debug/' + name
+
+ if not os.path.exists(tensor_dir):
+ os.makedirs(tensor_dir)
+
+ tensor_name = tensor_dir + "{}.npy".format(name)
+ np.save(tensor_name, tensor)
+ comp_hist_tensor3d(x=tensor, name=name, en=True, dir=name, log=True, ch_dim=0)
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