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Rangement
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Harle, Antoine (Contracteur)
parent
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16 changed files with 85 additions and 46 deletions
214
higher/utils.py
214
higher/utils.py
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@ -1,214 +0,0 @@
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import numpy as np
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import json, math, time, os
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import matplotlib.pyplot as plt
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import copy
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import gc
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from torchviz import make_dot
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import torch
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import torch.nn.functional as F
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import time
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def print_graph(PyTorch_obj, fig_name='graph'):
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graph=make_dot(PyTorch_obj) #Loss give the whole graph
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graph.format = 'pdf' #https://graphviz.readthedocs.io/en/stable/manual.html#formats
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graph.render(fig_name)
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def plot_resV2(log, fig_name='res', param_names=None):
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epochs = [x["epoch"] for x in log]
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fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(30, 15))
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ax[0, 0].set_title('Loss')
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ax[0, 0].plot(epochs,[x["train_loss"] for x in log], label='Train')
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ax[0, 0].plot(epochs,[x["val_loss"] for x in log], label='Val')
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ax[0, 0].legend()
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ax[1, 0].set_title('Acc')
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ax[1, 0].plot(epochs,[x["acc"] for x in log])
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if log[0]["param"]!= None:
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if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])]
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#proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])]
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proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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ax[0, 1].set_title('Prob =f(epoch)')
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ax[0, 1].stackplot(epochs, proba, labels=param_names)
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#ax[0, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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ax[1, 1].set_title('Prob =f(TF)')
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mean = np.mean(proba, axis=1)
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std = np.std(proba, axis=1)
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ax[1, 1].bar(param_names, mean, yerr=std)
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plt.sca(ax[1, 1]), plt.xticks(rotation=90)
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ax[0, 2].set_title('Mag =f(epoch)')
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ax[0, 2].stackplot(epochs, mag, labels=param_names)
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#ax[0, 2].plot(epochs, np.array(mag).T, label=param_names)
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ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
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ax[1, 2].set_title('Mag =f(TF)')
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mean = np.mean(mag, axis=1)
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std = np.std(mag, axis=1)
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ax[1, 2].bar(param_names, mean, yerr=std)
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plt.sca(ax[1, 2]), plt.xticks(rotation=90)
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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def plot_compare(filenames, fig_name='res'):
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all_data=[]
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legend=""
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for idx, file in enumerate(filenames):
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legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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all_data.append(data)
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fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
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for data_idx, log in enumerate(all_data):
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log=log['Log']
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epochs = [x["epoch"] for x in log]
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ax[0].plot(epochs,[x["train_loss"] for x in log], label=str(data_idx)+'-Train')
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ax[0].plot(epochs,[x["val_loss"] for x in log], label=str(data_idx)+'-Val')
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ax[1].plot(epochs,[x["acc"] for x in log], label=str(data_idx))
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#ax[1].text(x=0.5,y=0,s=str(data_idx)+'-'+filenames[data_idx], transform=ax[1].transAxes)
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if log[0]["param"]!= None:
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if isinstance(log[0]["param"],float):
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ax[2].plot(epochs,[x["param"] for x in log], label=str(data_idx)+'-Mag')
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else :
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for idx, _ in enumerate(log[0]["param"]):
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ax[2].plot(epochs,[x["param"][idx] for x in log], label=str(data_idx)+'-P'+str(idx))
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fig.suptitle(legend)
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ax[0].set_title('Loss')
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ax[1].set_title('Acc')
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ax[2].set_title('Param')
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for a in ax: a.legend()
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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def plot_TF_res(log, tf_names, fig_name='res'):
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mean = np.mean([x["param"] for x in log], axis=0)
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std = np.std([x["param"] for x in log], axis=0)
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fig, ax = plt.subplots(1, 1, figsize=(30, 8), sharey=True)
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ax.bar(tf_names, mean, yerr=std)
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#ax.bar(tf_names, log[-1]["param"])
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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def viz_sample_data(imgs, labels, fig_name='data_sample', weight_labels=None):
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sample = imgs[0:25,].permute(0, 2, 3, 1).squeeze().cpu()
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plt.figure(figsize=(10,10))
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for i in range(25):
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plt.subplot(5,5,i+1) #Trop de figure cree ?
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(sample[i,].detach().numpy(), cmap=plt.cm.binary)
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label = str(labels[i].item())
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if weight_labels is not None : label+= (" - p %.2f" % weight_labels[i].item())
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plt.xlabel(label)
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plt.savefig(fig_name)
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print("Sample saved :", fig_name)
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plt.close('all')
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def print_torch_mem(add_info=''):
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nb=0
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max_size=0
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for obj in gc.get_objects():
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#print(type(obj))
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try:
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if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): # and len(obj.size())>1:
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#print(i, type(obj), obj.size())
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size = np.sum(obj.size())
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if(size>max_size): max_size=size
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nb+=1
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except:
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pass
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print(add_info, "-Pytroch tensor nb:",nb," / Max dim:", max_size)
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#print(add_info, "-Garbage size :",len(gc.garbage))
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"""Simple GPU memory report."""
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mega_bytes = 1024.0 * 1024.0
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string = add_info + ' memory (MB)'
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string += ' | allocated: {}'.format(
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torch.cuda.memory_allocated() / mega_bytes)
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string += ' | max allocated: {}'.format(
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torch.cuda.max_memory_allocated() / mega_bytes)
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string += ' | cached: {}'.format(torch.cuda.memory_cached() / mega_bytes)
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string += ' | max cached: {}'.format(
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torch.cuda.max_memory_cached()/ mega_bytes)
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print(string)
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def plot_TF_influence(log, fig_name='TF_influence', param_names=None):
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proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
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plt.figure()
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mean = np.mean(proba, axis=1)*np.mean(mag, axis=1) #Pourrait etre interessant de multiplier avant le mean
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std = np.std(proba, axis=1)*np.std(mag, axis=1)
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plt.bar(param_names, mean, yerr=std)
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plt.xticks(rotation=90)
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fig_name = fig_name.replace('.',',')
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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### https://github.com/facebookresearch/higher/issues/18 ####
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from torch._six import inf
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def clip_norm(tensors, max_norm, norm_type=2):
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r"""Clips norm of passed tensors.
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The norm is computed over all tensors together, as if they were
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concatenated into a single vector. Clipped tensors are returned.
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Arguments:
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tensors (Iterable[Tensor]): an iterable of Tensors or a
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single Tensor to be normalized.
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max_norm (float or int): max norm of the gradients
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norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
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infinity norm.
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Returns:
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Clipped (List[Tensor]) tensors.
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"""
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if isinstance(tensors, torch.Tensor):
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tensors = [tensors]
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tensors = list(tensors)
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max_norm = float(max_norm)
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norm_type = float(norm_type)
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if norm_type == inf:
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total_norm = max(t.abs().max() for t in tensors)
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else:
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total_norm = 0
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for t in tensors:
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param_norm = t.norm(norm_type)
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total_norm += param_norm.item() ** norm_type
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total_norm = total_norm ** (1. / norm_type)
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clip_coef = max_norm / (total_norm + 1e-6)
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if clip_coef >= 1:
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return tensors
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return [t.mul(clip_coef) for t in tensors]
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